The DOI matches its Crossref record; 37 of the 40 references it cites were found in a registry or an index; none of the 3 sources its claims cite is checked for support yet.
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
Open accessAbstract
Importance: Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting. Design, Setting, and Participants: Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures: Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures: The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results: The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). Conclusions and Relevance: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
38 Own result · 16 Own method · 10 Own interpretation · 4 No basis given · 3 Not checked yet
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At the slide level in task 2, the pathologist WOTC achieved a sensitivity of 93.8% (95% CI, 86.9%-100.0%), a specificity of 98.7% (95% CI, 96.0%-100.0%),and an AUC of 0.966 (95% CI, 0.927-0.998).”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On the slide level, the leading algorithms performed better than the pathologists WTC in the simulation exercise.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The simulated time-constrained exercise mimics routine diagnostic pathology workflow.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Pathologist WOTC | 0.724 | 0.966 | | | | Expert pathologist who assessed”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“They achieved a mean sensitivity of 62.8% (95% CI, 58.9%-71.9%)with a mean specificity of 98.5% (95% CI, 97.9%-99.1%).The mean AUC was 0.810 (range, 0.738-0.884)”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“No false-positives were produced in task 1 (ie, nontumorous tissue indicated as metastasis) by the pathologist WOTC”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This AUC exceeded the mean performance of the pathologists WTC (mean AUC, 0.810 [range, 0.738-0.884]) in the diagnostic simulation exercise (P < .001,calculated using MRMC ROC analysis 33 ) (Table 2).”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Task 2: Whole-Slide Image Classification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Algorithms based on manually engineered features performed less well.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At the slide level in task 2, the pathologist WOTC achieved a sensitivity of 93.8% (95% CI, 86.9%-100.0%), a specificity of 98.7% (95% CI, 96.0%-100.0%),and an AUC of 0.966 (95% CI, 0.927-0.998).”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This algorithm achieved an overall AUC of 0.976 (95% CI, 0.941-0.999),and yielded the highest AUC in detecting macrometastases (AUC, 1.0).”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Task 2: Whole-Slide Image Classification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 16
- The quote only partly states this claim. 3
- It may state two separate claims as one. 11
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“All of the 32 algorithms submitted to CAMELYON16 used a discriminative learning approach to identify metastases in whole-slide images.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Mean pathologists WTC | | 0.810 | | | | The mean performance of 11”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, deep learning-based algorithms performed significantly better than other methods: the 19 topperforming algorithms in both tasks all used deep convolutional neural networks as the underlying methodology (Table 2).”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Algorithm Performance
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“An earlier submission by this team, HMS and MGH I, achieved an overall AUC of 0.964 (95% CI, 0.928-0.989)and ranked third.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Task 2: Whole-Slide Image Classification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“but 27.6% of individual metastases were not identified (lesion level, truepositive fraction, 72.4% [95% CI, 64.3%-80.4%])that manifested when IHC staining was performed.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The pathologist did not produce any false-positives and achieved a true-positive fraction of 0.724 for detecting and localizing metastatic regions.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In cross-sectional analyses that evaluated 32 algorithms submitted as part of a challenge competition, 7 deep learning algorithms showed greater discrimination than a panel of 11 pathologists in a simulated time-constrained diagnostic setting, with an area under the curve of 0.994 (best algorithm) vs 0.884 (best pathologist).”
- Also stated in: § Task 2: Whole-Slide Image Classification · plain
“Overall, 7 of the 32 submitted algorithms had significantly higher AUCs than the pathologists WTC (see Table 2 for the individual P values calculated using MRMC ROC analysis).”
- Also stated in: § Discussion · boosted
“The CAMELYON16 challenge demonstrated that some deep learning algorithms were able to achieve a better AUC than a panel of 11 pathologists WTC participating in a simulation exercise for detection of lymph node metastases of breast cancer.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Key Points
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § Task 2: Whole-Slide Image Classification · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The best algorithm achieved similar true-positive fraction as the pathologist WOTC when producing a mean of 1.25 false-positive lesions in 100 whole-slide images and performed better when allowing for slightly more falsepositive findings.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Discussion
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The set of images was randomly divided into a training (n = 270) and a test set (n = 129; details in Table 1).”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Reference Standard
plainn = 270- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“system generalization and performance seemed more important.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Downloaded From: by a Radboud University Nijmegen User on 12/12/2017
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
In the paper
“Previous studies on diagnostic imaging tasks in which deep learning reached human-level performance, such as detection of diabetic retinopathy in retinal fundus photographs, used a reference standard based on the consensus of human experts.3”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Downloaded From: by a Radboud University Nijmegen User on 12/12/2017
What the sources say
- Not checked yetcites [3]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The test data set on which algorithms and pathologists were evaluated was enriched with cases containing metastases and, specifically, micrometastases and, thus, is not directly comparable with the mix of cases pathologists encounter in clinical practice.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Limitations
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Most sentinel lymph nodes do not contain metastases.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“pathologists may rely on IHC staining and the knowledge that all hematoxylin and eosin-slides with negative findings will undergo additional review with the use of IHC.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Limitations
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: In most hospitals, pathologists request additional immunohistochemical staining in equivocal cases.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Assessment without time constraint does not yield a fair measure of the accuracy of the routine diagnostic process.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Panel of Pathologists With Time Constraint
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“staining, yielding an independent reference against which human pathologists could also be compared.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Downloaded From: by a Radboud University Nijmegen User on 12/12/2017
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Less time constraint on task completion may increase the accuracy of SLN diagnostic review.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Limitations
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“algorithm runtime was not recorded nor included as a factor in the evaluation, but it might influence the suitability for use in, for example, frozen section analysis.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Limitations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“the simulation exercise invited pathologists WTC to review a series of 129 hematoxylin and eosinstained slides in about 2 hours to determine the presence of macroscopic or microscopic SLN metastasis.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Limitations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Thepercentilebootstrapmethod was used to construct 95% CIs for FROC true-positive fraction scores (FROC scores) and AUCs.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The funders and sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Funding
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The pathologists WTC assessed the same set of glass slides used for testing the algorithms (which used digitized whole-slide images of these glass slides).”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Panel of Pathologists With Time Constraint
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“IHC is the most accurate method for metastasis evaluation and has little interpretation variability. [7][8]][9]”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Reference Standard
What the sources say
- Not checked yetcites [7][8]][9]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“algorithms were specifically trained to discriminate between normal and cancerous tissue in the background of lymph node histological architecture, but they might be unable to identify rare events such as co-occurring pathologies (eg, lymphoma, sarcoma, or infection).”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Limitations
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The detection of other pathologies in the SLN, which is relevant in routine diagnostics, was not included in this study.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Limitations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“No prior data were available for the performance of algorithms in this task.Therefore, no power analysis was used to predetermine the sample size.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Statistical Analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: No prior data were available for algorithm performance in this task.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“In this study, every pathologist was given 1 single hematoxylin and eosin-stained slide per patient to determine the presence or absence of breast cancer metastasis.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Limitations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The P values were adjusted for multiple comparisons using the Bonferroni correction, in which the P values are multiplied by the number of comparisons (32; comparison of the 32 submitted algorithms with the panel of pathologists).”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although feasible in the context of this simulation, this does not represent the work pace in other settings.”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Limitations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
The clinical value of isolated tumor cells in a sentinel lymph node is disputed.
“Because the clinical value of having only isolated tumor cells in an SLN is disputed, we did not include such slides in our study”
The paper relies on this without support.
The simulated time-constrained exercise mimics routine diagnostic pathology workflow.
“To mimic routine diagnostic pathology workflow, we asked 11 pathologists to independently assess the 129 slides in the test set in”
The paper relies on this without support.
The panel of 11 pathologists is representative of pathologists in routine practice.
“The panel of the 11 pathologists (mean age, 47.7 years [range, 31-61]) included 1 resident pathologist (3-year resident) and 10 practicing pathologists”
The paper relies on this without support.
Comparing algorithms on whole-slide images with pathologists on glass slides is valid despite the different modalities.
“The panel of 11 pathologists with time constraint (pathologists WTC), however, did their assessment on the corresponding glass slides for those images because diagnosing is most commonly done using a microscope in pathology labs.”
The paper relies on this without support.
In the Netherlands, use of immunohistochemistry is mandatory for cases with negative findings on H&E-stained slides.
“The setting resembled diagnostic practice in the Netherlands, where use of IHC is mandatory for cases with negative findings on hematoxylin and eosin-stained slides.”
The paper relies on this without support.
No prior data were available for algorithm performance in this task.
“No prior data were available for the performance of algorithms in this task.Therefore, no power analysis was used to predetermine the sample size.”
The paper relies on this without support.
Most sentinel lymph nodes do not contain metastases.
“Given the reality that most SLNs do not contain metastases, the data set curation was needed to achieve a well-rounded representation of what is encountered in clinical practice without including an exorbitant number of slides.”
The paper relies on this without support.
In real clinical settings, sections from multiple levels are evaluated for every lymph node.
“In a real clinical setting, sections from multiple levels are evaluated for every lymph node.”
The paper relies on this without support.
In most hospitals, pathologists request additional immunohistochemical staining in equivocal cases.
“in most hospitals pathologists request additional IHC staining in equivocal cases.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Medical research is no exception, as CNN has achieved expert-level performances in various fields. Gulshan et al. [4], Esteva et al. [5], and Ehteshami Bejnordi et al. [6] demonstrated the potential of deep learning for diabetic retinopathy screening, skin lesion classification, and lymph node metastasis detection, respectively.”
From Convolutional neural networks: an overview and application in radiology (2018) · cites this paper as [6]
The cited source says what the claim attributes to it.
In the citing paper
“In a notable study, Bejnordi et al. (2017) found that certain deep learning algorithms surpassed a group of 11 pathologists in detecting lymph node metastases in breast cancer patients.”
From A perspective on integrating digital pathology, proteomics, clinical data and AI analytics in cancer research (2025) · cites this paper as [24]
In the citing paper
“This evaluation includes comparison with a reference panel of pathologists working on the same data set with predefined time constraints designed to mimic routine pathology workflows, where many of the models in the challenge exhibited higher performance than the panel of pathologists.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [45]
In the citing paper
“The second option is deidentification and public release of data, usually via a consortium model.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [45]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Downloaded From: by a Radboud University Nijmegen User on 12/12/2017
“Task 2 was measured on the 129 whole-slide images (for algorithms and the pathologist WTC) and corresponding glass slides (for 11 pathologists WOTC) in the test data set”
§ Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer > Task 1: Identification of Individual Metastases
“Algorithms assessed whole-slide images, as did the pathologist WOTC.The panel of 11 pathologists with time constraint (pathologists WTC), however, did their assessment on the corresponding glass slides”
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886
Cited by
Sorted by citations
Items 401–500 of 1,020
Corti, C., Cobanaj, M., Dee, E. C., Criscitiello, C., Tolaney, S. M., Celi, L. A., & Curigliano, G. (2022) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2022.102498 · Cited by 97
Springenberg, M., Frommholz, A., Wenzel, M. A., Weicken, E., Ma, J., & Strodthoff, N. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102809 · Cited by 106
Greaves, R. F., Bernardini, S., Ferrari, M., Fortina, P. M., Gouget, B., Gruson, D., Lang, T., Loh, T. P., Morris, H. A., Park, J. Y., et al. (2019) · Clinica Chimica Acta · doi:10.1016/j.cca.2019.05.021 · Cited by 92
UD-MIL: Uncertainty-Driven Deep Multiple Instance Learning for OCT Image Classification
Wang, X., Tang, F., Chen, H., Luo, L., Tang, Z., Ran, A. R., Cheung, C. Y.-L., & Heng, P. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2983730 · Cited by 91
Self-citationExplainable AI (xAI) for Anatomic Pathology
Tosun, A. B., Pullara, F., Becich, M. J., Taylor, D. L., Fine, J. L., & Chennubhotla, S. C. (2020) · Advances in Anatomic Pathology · doi:10.1097/pap.0000000000000264 · Cited by 88
Artificial intelligence in food science and nutrition: A narrative review
Miyazawa, T., Hiratsuka, Y., Toda, M., Hatakeyama, N., Ozawa, H., Abe, C., Cheng, T.-Y., Matsushima, Y., Miyawaki, Y., Ashida, K., et al. (2022) · Nutrition Reviews · doi:10.1093/nutrit/nuac033 · Cited by 91
Marra, A., Morganti, S., Pareja, F. G., Campanella, G. S. V., Bibeau, F., Fuchs, T. J., Loda, M. F., Parwani, A. V., Scarpa, A., Reis-Filho, J. S., et al. (2025) · Annals of Oncology · doi:10.1016/j.annonc.2025.03.006 · Cited by 114
The impact of artificial intelligence in the diagnosis and management of glaucoma
Mayro, E. L., Wang, M., Elze, T., & Pasquale, L. R. (2019) · Eye · doi:10.1038/s41433-019-0577-x · Cited by 91
A Fast and Refined Cancer Regions Segmentation Framework in Whole-slide Breast Pathological Images
Guo, Z., Liu, H., Ni, H., Wang, X., Su, M., Guo, W., Wang, K., Jiang, T., & Qian, Y. (2019) · Scientific Reports · doi:10.1038/s41598-018-37492-9 · Cited by 88
Deep computational pathology in breast cancer
Duggento, A., Conti, A., Mauriello, A., Guerrisi, M., & Toschi, N. (2020) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2020.08.006 · Cited by 93
Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoring
Qaiser, T., & Rajpoot, N. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2907049 · Cited by 96
Automated interpretation of congenital heart disease from multi-view echocardiograms
Wang, J., Liu, X., Wang, F., Zheng, L., Gao, F., Zhang, H., Zhang, X., Xie, W., & Wang, B. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101942 · Cited by 89
Introduction to Artificial Intelligence and Machine Learning for Pathology
Harrison, J. H., Gilbertson, J. R., Hanna, M. G., Olson, N. H., Seheult, J. N., Sorace, J. M., & Stram, M. N. (2021) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2020-0541-cp · Cited by 91
Digital Health Applications in Oncology: An Opportunity to Seize
Parikh, R. B., Basen-Enquist, K. M., Bradley, C. J., Estrin, D. L., Levy, M. A., Lichtenfeld, J. L., Malin, B., McGraw, D., Meropol, N. J., Oyer, R. A., et al. (2022) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djac108 · Cited by 81
Deep learning models for histologic grading of breast cancer and association with disease prognosis
Jaroensri, R., Wulczyn, E., Hegde, N., Brown, T., Flament-Auvigne, I., Tan, F. E., Cai, Y., Nagpal, K., Rakha, E. A., Dabbs, D. J., et al. (2022) · npj Breast Cancer · doi:10.1038/s41523-022-00478-y · Cited by 84
Zhang, Y., Liu, Y.-L., Nie, K., Zhou, J., Chen, Z., Chen, J., Wang, X., Kim, B., Parajuli, R., Mehta, R. S., et al. (2023) · Academic Radiology · doi:10.1016/j.acra.2022.12.038 · Cited by 93
Sheikh, T. S., Lee, Y., & Cho, M. (2020) · Cancers · doi:10.3390/cancers12082031 · Cited by 86
Zhou, C., Jin, Y., Chen, Y., Huang, S., Huang, R., Wang, Y., Zhao, Y., Yao, C., Guo, L., & Liao, J. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101861 · Cited by 86
Machine Learning in Health Care: A Critical Appraisal of Challenges and Opportunities
Sendak, M., Gao, M. C., Nichols, M., Lin, A. L., & Balu, S. (2019) · eGEMs (Generating Evidence & Methods to improve patient outcomes) · doi:10.5334/egems.287 · Cited by 87
Weakly Supervised Multiple Instance Learning Histopathological Tumor Segmentation
Lerousseau, M., Vakalopoulou, M., Classe, M., Adam, J., Battistella, E., Carré, A., Estienne, T., Henry, T., Deutsch, É., & Nikos Paragios, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59722-1_45 · Cited by 109
Kandel, I., Castelli, M., & Popovič, A. (2020) · Journal of Imaging · doi:10.3390/jimaging6090092 · Cited by 95
Deep learning extended depth-of-field microscope for fast and slide-free histology
Jin, L., Tang, Y., Wu, Y., Coole, J. B., Tan, M. T., Zhao, X., Badaoui, H., Robinson, J. T., Williams, M. D., Gillenwater, A. M., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2013571117 · Cited by 87
Computational pathology in cancer diagnosis, prognosis, and prediction – present day and prospects
Verghese, G. E., Lennerz, J. K., Ruta, D. A., Ng, W., Thavaraj, S., Siziopikou, K. P., Naidoo, T., Rane, S. U., Salgado, R., Pinder, S. E., et al. (2023) · The Journal of Pathology · doi:10.1002/path.6163 · Cited by 94
BMAD: Benchmarks for Medical Anomaly Detection
Bao, J., Sun, H., Deng, H., He, Y., Zhang, Z., & Li, X. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00408 · Cited by 122
New Developments in Hemodynamic Monitoring
Scheeren, T., & Ramsay, M. A. E. (2019) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2019.03.043 · Cited by 84
Roto-translation equivariant convolutional networks: Application to histopathology image analysis
Lafarge, M. W., Bekkers, E. J., Pluim, J. P. W., Duits, R., & Veta, M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101849 · Cited by 84
Self-citationSurgery 3.0, artificial intelligence and the next‐generation surgeon
Mirnezami, R., & Ahmed, A. R. (2018) · British Journal of Surgery · doi:10.1002/bjs.10860 · Cited by 73
Mercan, E., Mehta, S., Bartlett, J. R., et al. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.8777 · Cited by 85
Liu, M., Jiang, J., & Wang, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2921027 · Cited by 81
FairLens: Auditing black-box clinical decision support systems
Panigutti, C., Perotti, A., Panisson, A., Bajardi, P., & Pedreschi, D. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102657 · Cited by 86
Artificial Intelligence: A Promising Tool for Application in Phytopathology
González‐Rodríguez, V. E., Izquierdo‐Bueno, I., Cantoral, J. M., Carbú, M., & Garrido, C. (2024) · Horticulturae · doi:10.3390/horticulturae10030197 · Cited by 104
Yang, Z., Wei, T., Liang, Y., Yuan, X., Gao, R., Xia, Y., Zhou, J., Zhang, Y., & Yu, Z. (2025) · Nature Communications · doi:10.1038/s41467-025-57587-y · Cited by 110
Kim, H., Kang, W. S., Park, H. J., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-36404-1 · Cited by 82
Otálora, S., Atzori, M., Andrearczyk, V., Khan, A., & Müller, H. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00198 · Cited by 82
Wang, X., Chen, H., Ran, A. R., Luo, L., Chan, P. P. M., Tham, C. C. Y., Chang, R. T., Mannil, S. S., Cheung, C. Y.-L., & Heng, P. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101695 · Cited by 82
Self-citationChen, S., Jiang, L., Gao, F., Zhang, E., Wang, T., Zhang, N., Wang, X., & Zheng, J. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01640-2 · Cited by 75
Wang, G., Yao, H., Gong, Y., Lu, Z., Pang, R., Li, Y., Yuan, Y., Song, H., Liu, J., Jin, Y., et al. (2021) · Science Advances · doi:10.1126/sciadv.abh2724 · Cited by 86
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
Graham, S., Epstein, D. B. A., & Rajpoot, N. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3013246 · Cited by 87
Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning
Wakili, M. A., Shehu, H. A., Sharif, M. H., Sharif, M. H. U., Umar, A., Kusetoğulları, H., İnce, İ. F., & Uyaver, Ş. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/8904768 · Cited by 94
Lazard, T., Bataillon, G., Naylor, P., Popova, T. G., Bidard, F., Stoppa‐Lyonnet, D., Stern, M., Decencière, É., Walter, T., & Vincent‐Salomon, A. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100872 · Cited by 80
Li, Z., Jiang, Y., Lu, M., Li, R., & Xia, Y. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3263010 · Cited by 93
Machine learning detection of Atrial Fibrillation using wearable technology
Lown, M. T., Brown, M. E., Brown, C., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0227401 · Cited by 81
Jiao, Y., Li, J., Qian, C., & Fei, S. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106047 · Cited by 83
Brockmoeller, S. F., Echle, A., Laleh, N. G., Eiholm, S., Malmstrøm, M. L., Kühlmann, T. P., Levic, K., Grabsch, H. I., West, N. P., Saldanha, O. L., et al. (2021) · The Journal of Pathology · doi:10.1002/path.5831 · Cited by 78
High-dimensional role of AI and machine learning in cancer research
Capobianco, E. (2022) · British Journal of Cancer · doi:10.1038/s41416-021-01689-z · Cited by 76
Assessment of deep learning assistance for the pathological diagnosis of gastric cancer
Ba, W., Wang, S., Shang, M., Zhang, Z., Wu, H. G., Yu, C., Xing, R., Wang, W., Wang, L., Liu, C., et al. (2022) · Modern Pathology · doi:10.1038/s41379-022-01073-z · Cited by 76
Shen, Z., Hu, J., Wu, H., Chen, Z., Wu, W., Lin, J., Xu, Z., Kong, J., & Lin, T. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03615-0 · Cited by 81
Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning
Chan, T. H., Cendra, F. J., Ma, L., Yin, G., & Yu, L. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01503 · Cited by 87
Wang, L., Ding, L., Liu, Z., et al. (2019) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2018-313706 · Cited by 79
Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images
Steinbuß, G., Kriegsmann, M., Zgorzelski, C., Brobeil, A., Goeppert, B. F., Dietrich, S., Mechtersheimer, G., & Kriegsmann, K. (2021) · Cancers · doi:10.3390/cancers13102419 · Cited by 85
Current challenges in metastasis research and future innovation for clinical translation
Parker, A. L., Benguigui, M., Fornetti, J., Goddard, E. T., Lucotti, S., Insua‐Rodríguez, J., & Wiegmans, A. P. (2022) · Clinical & Experimental Metastasis · doi:10.1007/s10585-021-10144-5 · Cited by 82
Balaha, H. M., Saif, M., Tamer, A., & Abdelhay, E. H. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06851-5 · Cited by 79
Deep Learning Approaches in Histopathology
Ahmed, A. A., Abouzid, M., & Kaczmarek, E. (2022) · Cancers · doi:10.3390/cancers14215264 · Cited by 85
Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors
Li, Z., Cong, Y., Chen, X., Qi, J., Sun, J., Yan, T., Yang, H., Liu, J., Lu, E., Wang, L., et al. (2023) · iScience · doi:10.1016/j.isci.2022.105872 · Cited by 82
Attitudes Of Chinese Cancer Patients Toward The Clinical Use Of Artificial Intelligence
Yang, K., Zeng, Z., Peng, H., & Jiang, Y. (2019) · Patient Preference and Adherence · doi:10.2147/ppa.s225952 · Cited by 74
Johnston, K., & Baker, J. R. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02925 · Cited by 101
How Deeply to Fine-Tune a Convolutional Neural Network: A Case Study Using a Histopathology Dataset
Kandel, I., & Castelli, M. (2020) · Applied Sciences · doi:10.3390/app10103359 · Cited by 85
Sun, P., He, J., Chao, X., Chen, K., Xu, Y., Huang, Q., Yun, J., Li, M., Luo, R., Kuang, J., et al. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103492 · Cited by 78
Developing image analysis methods for digital pathology
Bankhead, P. (2022) · The Journal of Pathology · doi:10.1002/path.5921 · Cited by 76
Qu, L., Liu, S., Liu, X., Wang, M., & Song, Z. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac910a · Cited by 89
Cancer Metastasis Detection With Neural Conditional Random FieldPreprint
Li, Y., & Ping, W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.07064 · Cited by 92
Artificial intelligence & clinical nutrition: What the future might have in store
Bond, A., Mccay, K., & Lal, S. (2023) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2023.07.082 · Cited by 79
A Deep Learning Approach for Meibomian Gland Atrophy Evaluation in Meibography Images
Wang, J., Yeh, T. N., Chakraborty, R., Yu, S. X., & Lin, M. C. (2019) · Translational Vision Science & Technology · doi:10.1167/tvst.8.6.37 · Cited by 78
Artificial Intelligence for Cataract Detection and Management
Goh, J. H. L., Lim, Z. W., Fang, X., Anees, A., Nusinovici, S., Rim, T. H., Cheng, C., & Tham, Y. C. (2020) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/01.apo.0000656988.16221.04 · Cited by 80
Naito, Y., Tsuneki, M., Fukushima, N., Koga, Y., Higashi, M., Notohara, K., Aishima, S., Ohike, N., Tajiri, T., Yamaguchi, H., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-87748-0 · Cited by 77
Jiang, Y., Xiong, J., Li, H. Y., Yang, X., Yu, W. T., Gao, M., Zhao, X., Ma, Y., Zhang, W., Guan, Y. F., et al. (2019) · British Journal of Dermatology · doi:10.1111/bjd.18026 · Cited by 73
Jin, C., Jiang, Y., Yu, H., Wang, W., Li, B., Chen, C. L., Yuan, Q., Hu, Y., Xu, Y., Zhou, Z., et al. (2020) · British journal of surgery · doi:10.1002/bjs.11928 · Cited by 74
Deep learning can predict lymph node status directly from histology in colorectal cancer
Kiehl, L., Kuntz, S., Höhn, J., Jutzi, T. B., Krieghoff‐Henning, E., Kather, J. N., Holland‐Letz, T., Kopp‐Schneider, A., Chang-Claude, J. C., Brobeil, A., et al. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.08.039 · Cited by 79
Artificial intelligence: A promising frontier in bladder cancer diagnosis and outcome prediction
Borhani, S., Borhani, R., & Kajdacsy‐Balla, A. (2022) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2022.103601 · Cited by 79
Hu, W., Li, X., Li, C., Li, R., Jiang, T., Sun, H., Huang, X., Grzegorzek, M., & Li, X. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107034 · Cited by 86
Machine learning in computational histopathology: Challenges and opportunities
Cooper, M. C., Ji, Z., & Krishnan, R. G. (2023) · Genes Chromosomes and Cancer · doi:10.1002/gcc.23177 · Cited by 87
Breast Cancer Detection From Histopathological Images Using Deep Learning
Khuriwal, N., & Mishra, N. (2018) · IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE) · doi:10.1109/icraie.2018.8710426 · Cited by 80
Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm
Williams, S. C., Horsfall, H. L., Funnell, J. P., HANRAHAN, J. G., Khan, D. Z., Muirhead, W. R., Stoyanov, D. V., & Marcus, H. J. (2021) · Cancers · doi:10.3390/cancers13195010 · Cited by 80
Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews
Mäkitie, A. A., Alabi, R. O., Ng, S. P., Takes, R. P., Robbins, K. T., Ronen, O., Shaha, A. R., Bradley, P. J., Saba, N. F., Nuyts, S., et al. (2023) · Advances in Therapy · doi:10.1007/s12325-023-02527-9 · Cited by 85
Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning
Tian, F., Liu, D., Wei, N., Fu, Q., Sun, L., Liu, W., Sui, X., Tian, K., Nemeth, G., Feng, J., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02915-w · Cited by 80
Machine learning methods for histopathological image analysis: updates in 2024
Komura, D., Ochi, M., & Ishikawa, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.033 · Cited by 76
Predictive uncertainty estimation for out-of-distribution detection in digital pathology
Linmans, J., Elfwing, S., van der Laak, J., & Litjens, G. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102655 · Cited by 75
Shao, J., Ma, J., Zhang, Q., Li, W., & Wang, C. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.02.006 · Cited by 85
Priego, B., Morillo, D., Fernandez-Granero, M. A., & García‐Rojo, M. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113387 · Cited by 73
Machine Learning in Oncology: What Should Clinicians Know?
Nagy, M. R., Radakovich, N., & Nazha, A. (2020) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00049 · Cited by 75
M$^3$Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening From CT Imaging
Qian, X., Fu, H., Shi, W., Chen, T., Fu, Y., Shan, F., & Xue, X. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3030853 · Cited by 72
Liang, W. H., Federico, S. M., London, W. B., et al. (2020) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00074 · Cited by 74
Xue, C., Yu, L., Chen, P., Dou, Q., & Heng, P.-A. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3140140 · Cited by 77
Self-citationMURA Dataset: Towards Radiologist-Level Abnormality Detection in Musculoskeletal RadiographsPreprint
Rajpurkar, P., Irvin, J., Bagul, A., Ding, D. Y., Duan, T., Mehta, H., Yang, B., Zhu, K., Laird, D., Ball, R. L., et al. (2017) · arXiv (Cornell University) · Cited by 83
Luo, Y., Chen, X., Chen, J., Song, C., Shen, J., Xiao, H., Chen, M., Li, Z.-P., Huang, B., & Feng, S. (2019) · Neuroendocrinology · doi:10.1159/000503291 · Cited by 71
Basic of machine learning and deep learning in imaging for medical physicists
Manco, L., Maffei, N., Strolin, S., Vichi, S., Bottazzi, L., & Strigari, L. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.03.026 · Cited by 75
Gao, Z., Hong, B., Li, Y., Zhang, X., Wu, J., Wang, C., Zhang, X., Gong, T., Zheng, Y., Meng, D., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102652 · Cited by 73
Is Grad-CAM Explainable in Medical Images?
Suara, S., Jha, A., Sinha, P., & Sekh, A. A. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-58181-6_11 · Cited by 99
Hwang, E. J., & Park, C. M. (2020) · Korean Journal of Radiology · doi:10.3348/kjr.2019.0821 · Cited by 77
A Petri Dish for Histopathology Image Analysis
Wei, J., Suriawinata, A. A., Ren, B., Liu, X., Lisovsky, M., Vaickus, L. J., Brown, C. L., Baker, M. L., Tomita, N., Torresani, L., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77211-6_2 · Cited by 93
Abimannan, S., El-Alfy, E.-S. M., Hussain, S., Chang, Y., Shukla, S., Satheesh, D., & Breslin, J. G. (2023) · Sustainability · doi:10.3390/su151813951 · Cited by 72
Deep transfer learning-based hologram classification for molecular diagnostics
Kim, S.-J., Wang, C., Zhao, B., Im, H., Min, J., Choi, H. J., Tadros, J., Choi, N. R., Castro, C. M., Weissleder, R., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-35274-x · Cited by 70
Diagnostic accuracy of a deep learning approach to calculate FFR from coronary CT angiography.
Wang, Z.-Q., Zhou, Y.-J., Zhao, Y., et al. (2019) · PubMed · doi:10.11909/j.issn.1671-5411.2019.01.010 · Cited by 71
Utilizing Knowledge Distillation in Deep Learning for Classification of Chest X-Ray Abnormalities
Ho, T. K. K., & Gwak, J. (2020) · IEEE Access · doi:10.1109/access.2020.3020802 · Cited by 72
Kumar, R. R., & Priyadarshi, R. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19313-6 · Cited by 88
Ahn, B., Moon, D., Kim, H.-S., Lee, C., Cho, N. H., Choi, H., Kim, D., Lee, J.-Y., Nam, E. J., Won, D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48667-6 · Cited by 74
Hallmarks of artificial intelligence contributions to precision oncology
Chang, T., Park, S., Schäffer, A. A., Jiang, P., & Ruppin, E. (2025) · Nature Cancer · doi:10.1038/s43018-025-00917-2 · Cited by 83
Burçak, K. C., Baykan, Ö. K., & Uğuz, H. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-020-03321-y · Cited by 70
Zhang, Y., Chan, S., Park, V. Y., Chang, K.-T., Mehta, S., Kim, M. J., Combs, F. J., Chang, P., Chow, D. S., Parajuli, R., et al. (2020) · Academic Radiology · doi:10.1016/j.acra.2020.12.001 · Cited by 72
Chuang, W., Chen, C., Yu, W.-H., Yeh, C., Chang, S., Ueng, S.-H., Wang, T., Hsueh, C., Kuo, C., & Yeh, C. (2021) · Modern Pathology · doi:10.1038/s41379-021-00838-2 · Cited by 67
Metadata
- Authors
- Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., Balkenhol, M., Geessink, O., Stathonikos, N., van Dijk, M. C., Bult, P., Beca, F., Beck, A. H., Wang, D., Khosla, A., Gargeya, R., Irshad, H., Zhong, A., Dou, Q., Li, Q., Chen, H., Lin, H.-J., Heng, P.-A., Haß, C., Bruni, E., Wong, Q., Halici, U., Öner, M. Ü., Cetin-Atalay, R., Berseth, M., Khvatkov, V., Vylegzhanin, A., Kraus, O., Shaban, M., Rajpoot, N., Awan, R., Sirinukunwattana, K., Qaiser, T., Tsang, Y.-W., Tellez, D., Annuscheit, J., Hufnagl, P., Valkonen, M., Kartasalo, K., Latonen, L., Ruusuvuori, P., Liimatainen, K., Albarqouni, S., Mungal, B., George, A., Demirci, S., Navab, N., Watanabe, S., Seno, S., Takenaka, Y., Matsuda, H., Ahmady Phoulady, H., Kovalev, V., Kalinovsky, A., Liauchuk, V., Bueno, G., Fernandez-Carrobles, M. M., Serrano, I., Deniz, O., Racoceanu, D., & Venâncio, R.
- Affiliations
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
- Eindhoven University of Technology (NL)
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
- Radboud University Medical Center (NL)
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
- University Medical Center Utrecht (NL)
- Rijnstate Hospital, Arnhem, the Netherlands
- BeckLab, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
- Beth Israel Deaconess Medical Center (US)
- PathAI (United States) (US)
- Harker School, San Jose, California
- Harvard University (US)
- Chinese University of Hong Kong (HK)
- Chinese University of Hong Kong, Hong Kong, China
- ExB Research and Development GmbH, Munich, Germany
- Munich Business School (DE)
- Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey
- Middle East Technical University (TR)
- Cancer System Biology Laboratory, Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
- NLP LOGIX, Jacksonville, Florida
- Smart Imaging Technologies, Houston, Texas
- University of Toronto (CA)
- University of Warwick (GB)
- University Hospital Coventry (GB)
- Qatar University (QA)
- HTW Berlin - University of Applied Sciences (DE)
- Tampere University of Technology (FI)
- Tampere University (FI)
- Technical University of Munich (DE)
- The University of Osaka (JP)
- University of South Florida, Tampa, Florida
- National Academy of Sciences of Belarus (BY)
- University of Castilla-La Mancha (ES)
- Visilab, University of Castilla-La Mancha, Ciudad Real, Spain
- Inserm (FR)
- Sorbonne Université (FR)
- ORCID
- Veta, Mitko
- Karssemeijer, Nico
- Litjens, Geert
- Hermsen, Meyke
- Manson, Quirine F
- Balkenhol, Maschenka
- Geessink, Oscar
- Bult, Peter
- Beck, Andrew H
- Irshad, Humayun
- Zhong, Aoxiao
- Dou, Qi
- Li, Quanzheng
- Chen, Hao
- Heng, Pheng-Ann
- Öner, Mustafa Ümit
- Kraus, Oren
- Shaban, Muhammad
- Rajpoot, Nasir
- Awan, Ruqayya
- Sirinukunwattana, Korsuk
- Qaiser, Talha
- Tsang, Yee-Wah
- Tellez, David
- Annuscheit, Jonas
- Hufnagl, Peter
- Kartasalo, Kimmo
- Latonen, Leena
- Ruusuvuori, Pekka
- Liimatainen, Kaisa
- Albarqouni, Shadi
- Navab, Nassir
- Seno, Shigeto
- Matsuda, Hideo
- Kovalev, Vassili
- Liauchuk, Vitali
- Bueno, Gloria
- Serrano, Ismael
- Racoceanu, Daniel
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2017-12-12
- Volume
- 318
- Issue
- 22
- Pages
- 2199
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- PMC
- PMC5820737
- OpenAlex
- W2772723798
- Semantic Scholar
- ba913e2c03ece1c75f0af4d16dd11c7ffbc6e3ba
- Cited by
- 3,293
- References
- 40
- Citations by year
- 2026: 301 · 2025: 388 · 2024: 430 · 2023: 414 · 2022: 429 · 2021: 457 · 2020: 421 · 2019: 321 · 2018: 119 · 2017: 4
- FWCI
- 141.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- Locations
- JAMA (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- HAL (submittedVersion, other-oa)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- OpenMETU (Middle East Technical University) (submittedVersion, cc-by-nc-nd)
- TU/e Research Portal (submittedVersion)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Funders
- European Commission (601040)
- Japan Society for the Promotion of Science (15K00403)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- lymph node metastasis, breast cancer, whole slide images, deep learning, computer-aided diagnosis, H&E staining, diagnostic accuracy, immunohistochemistry validation, breast cancer staging, lymph node staging, false positives, diagnostic confidence
- MeSH terms
- Machine Learning, Pathologists, Algorithms, Breast Neoplasms, Female, Humans, Lymphatic Metastasis, Pathology, Clinical, ROC Curve
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
