CoChat
Sign Up
Cited by 232AI in cancer detectionRadiomics and Machine Learning in Medical ImagingMRI in cancer diagnosis

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

Prostate Cancer Detection using Deep Convolutional Neural Networks

Open access
Yoo, Sunghwan, Gujrathi, Isha, Haider, Masoom A., Khalvati, Farzad·2019·Scientific Reports
DOI 10.1038/s41598-019-55972-4PMC PMC6925141
Open this paper in CoChat — free accountOpen PDF

Abstract

Prostate cancer is one of the most common forms of cancer and the third leading cause of cancer death in North America. As an integrated part of computer-aided detection (CAD) tools, diffusion-weighted magnetic resonance imaging (DWI) has been intensively studied for accurate detection of prostate cancer. With deep convolutional neural networks (CNNs) significant success in computer vision tasks such as object detection and segmentation, different CNN architectures are increasingly investigated in medical imaging research community as promising solutions for designing more accurate CAD tools for cancer detection. In this work, we developed and implemented an automated CNN-based pipeline for detection of clinically significant prostate cancer (PCa) for a given axial DWI image and for each patient. DWI images of 427 patients were used as the dataset, which contained 175 patients with PCa and 252 patients without PCa. To measure the performance of the proposed pipeline, a test set of 108 (out of 427) patients were set aside and not used in the training phase. The proposed pipeline achieved area under the receiver operating characteristic curve (AUC) of 0.87 (95[Formula: see text] Confidence Interval (CI): 0.84-0.90) and 0.84 (95[Formula: see text] CI: 0.76-0.91) at slice level and patient level, respectively.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41598-019-55972-4

Cites

42 of 42 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Block diagram of the proposed pipeline for prostate cancer detection. The inputs to each CNN are 66 × 66 × 6 (ADC, b0, b100, b400, b1000, b1600) MRI slices. The output is the slice level and patient level results.
Figure 1 Block diagram of the proposed pipeline for prostate cancer detection. The inputs to each CNN are 66 × 66 × 6 (ADC, b0, b100, b400, b1000, b1600) MRI slices. The output is the slice level and patient level results.CC BY · PubMed Central
Figure 2 The structural difference between original residual network and fully pre-activated residual network.
Figure 2 The structural difference between original residual network and fully pre-activated residual network.CC BY · PubMed Central
Figure 3 Block diagram of the proposed first-order statistical feature extractor. PCa Set: probabilistic output set from each CNN which is associated with PCa class. Non PCa Set: probabilistic output set from each CNN which is associated with non PCa class.
Figure 3 Block diagram of the proposed first-order statistical feature extractor. PCa Set: probabilistic output set from each CNN which is associated with PCa class. Non PCa Set: probabilistic output set from each CNN which is associated with non PCa class.CC BY · PubMed Central
Figure 4 Slice-level ROC curve of the proposed ResNet inspired deep learning architecture (AUC: 0.87, CI: 0.84–0.90).
Figure 4 Slice-level ROC curve of the proposed ResNet inspired deep learning architecture (AUC: 0.87, CI: 0.84–0.90).CC BY · PubMed Central
Figure 5 Patient-level ROC curve of the proposed pipeline: Random Forest classifier trained on the features extracted by the CNNs (AUC: 0.84, CI: 0.76–0.91).
Figure 5 Patient-level ROC curve of the proposed pipeline: Random Forest classifier trained on the features extracted by the CNNs (AUC: 0.84, CI: 0.76–0.91).CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 4

  1. Michalski, R. S., Carbonell, J. G. & Mitchell, T. M. Machine learning: An artificial intelligence approach (Springer Science & Business Media, 2013).
  2. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. nature 521, 436 (2015). · DOI · Paper page
  3. Vapnik, V.The nature of statistical learning theory (Springer science and business media, 2013).
  4. Brinkmann, R.The art and science of digital compositing: Techniques for visual effects, animation and motion graphics (Morgan Kaufmann, 2008).

Cited by

Sorted by citations

See all 232

Related

See all

Metadata

Authors
Yoo, S., Gujrathi, I., Haider, M. A., & Khalvati, F.
Affiliations
  • Lunenfeld-Tanenbaum Research Institute (CA)
  • University of Toronto (CA)
Corresponding author
Khalvati, Farzad
ORCID
Haider, Masoom A.
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2019-12-20
Volume
9
Issue
1
Pages
19518-19518
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-019-55972-4
PMC
PMC6925141
OpenAlex
W2995218128
Cited by
230
References
39
Citations by year
2026: 14 · 2025: 31 · 2024: 37 · 2023: 36 · 2022: 42 · 2021: 50 · 2020: 17 · 2019: 3
FWCI
12.77
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Ontario Institute for Cancer Research
  • Government of Ontario
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 › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
Keywords
prostate cancer detection, clinically significant prostate cancer, diffusion-weighted imaging, computer-aided detection, deep convolutional neural networks, medical image classification
MeSH terms
Deep Learning, Humans, Male, Prostatic Neoplasms, ROC Curve, Neural Networks, Computer, Diffusion Magnetic Resonance Imaging

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.

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