One step further into the blackbox: A pilot study of how to build more confidence around an AI-based decision system of breast nodule assessment in 2D ultrasound
Abstract
Objectives To investigate how a DL model makes decisions in lesion classification with a newly defined region of evidence (ROE) by incorporating "explainable AI" (xAI) techniques.Methods A data set of 785 2D breast ultrasound images acquired from 367 females. The DenseNet-121 was used to classify whether the lesion is benign or malignant. For performance assessment, classification results are evaluated by calculating accuracy, sensitivity, specificity, and receiver operating characteristic for experiments of both coarse and fine regions of interest (ROIs). The area under the curve (AUC) was evaluated, and the true-positive, false-positive, true-negative, and false-negative results with breakdown in high, medium, and low resemblance on test sets were also reported.Results The two models with coarse and fine ROIs of ultrasound images as input achieve an AUC of 0.899 and 0.869, respectively. The accuracy, sensitivity, and specificity of the model with coarse ROIs are 88.4%, 87.9%, and 89.2%, and with fine ROIs are 86.1%, 87.9%, and 83.8%, respectively. The DL model captures ROE with high resemblance of physicians' consideration as they assess the image.Conclusions We have demonstrated the effectiveness of using DenseNet to classify breast lesions with limited quantity of 2D grayscale ultrasound image data. We have also proposed a new ROE-based metric system that can help physicians and patients better understand how AI makes decisions in reading images, which can potentially be integrated as a part of evidence in early screening or triaging of patients undergoing breast ultrasound examinations.Key points • The two models with coarse and fine ROIs of ultrasound images as input achieve an AUC of 0.899 and 0.869, respectively. The accuracy, sensitivity, and specificity of the model with coarse ROIs are 88.4%, 87.9%, and 89.2%, and with fine ROIs are 86.1%, 87.9%, and 83.8%, respectively. • The first model with coarse ROIs is slightly better than the second model with fine ROIs according to these evaluation metrics. • The results from coarse ROI and fine ROI are consistent and the peripheral tissue is also an impact factor in breast lesion classification.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Dong et al. promoted the importance of an increased confidence in AI assistance in health care, which can be addressed by understanding the algorithm of the black box and encouraging the concept of 'explainable AI'.
“Dong et al. promoted the importance of an increased confidence in AI assistance in health care, which can be addressed by understanding the algorithm of the black box and encouraging the concept of ‘explainable AI’ [201].”
Lower accuracy in borderline findings was a limitation to AI usage in breast US.
“a lower accuracy in borderline findings [201].”
Some AI models can fail in pattern recognition tasks when subtle differences are diagnosis-relevant, e.g., in borderline findings or in small regions of interest such as endometrial thickness or fetal brain structures.
“some AI models can fail when subtle differences are diagnosis-relevant, e.g., in borderline findings or in small regions of interest such as endometrial thickness or fetal brain structures [95,201,208].”
Explainable AI is used in the studies of Sakai et al. and Dong et al.
“the concept of ‘explainable AI’, which is used in the studies of Sakai et al. and Dong et al. [7,201].”
Cites
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513 · see citing works
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649 · see citing works
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · 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/cvpr.1993.341010 · Cited by 14,474 · see citing works
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117 · see citing works
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248 · see citing works
2011 International Conference on Computer Vision
(2011) · doi:10.1109/iccv17851.2011 · Cited by 2,895 · see citing works
2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
(2001) · 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/cvpr.2001.990916 · Cited by 1,884 · see citing works
Zeiler, M. D., Krishnan, D., Taylor, G. W., & Fergus, R. (2010) · 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/cvpr.2010.5539957 · Cited by 1,753 · see citing works
Greenspan, H. K., van Ginneken, B., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2553401 · Cited by 1,704 · see citing works
Machine learning for medical imaging
Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L. (2017) · RadioGraphics · doi:10.1148/rg.2017160130 · Cited by 1,672 · see citing works
Berg, W. A., Gutierrez, L., NessAiver, M. S., Carter, W. B., Bhargavan, M., Lewis, R. S., & Ioffe, O. B. (2004) · Radiology · doi:10.1148/radiol.2333031484 · Cited by 1,441 · see citing works
Adaptive deconvolutional networks for mid and high level feature learning
Zeiler, M. D., Taylor, G. W., & Fergus, R. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126474 · Cited by 1,265 · see citing works
Lee, C., Dershaw, D. D., Kopans, D. B., Evans, P., Monsees, B., Monticciolo, D. L., Brenner, R. J., Bassett, L. W., Berg, W. A., Feig, S. A., et al. (2010) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2009.09.022 · Cited by 812 · see citing works
Mittelstadt, B., Russell, C., & Wachter, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287574 · Cited by 794 · see citing works
Miller, A. B., Baines, C. J., To, T. M., & Wall, C. (1992) · Europe PMC (PubMed Central) · Cited by 744 · see citing works
Deep Learning—A Technology With the Potential to Transform Health Care
Hinton, G. (2018) · JAMA · doi:10.1001/jama.2018.11100 · Cited by 667 · see citing works
Sun, X., & Xu, W. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2014.2337313 · Cited by 638 · see citing works
AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images
Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S., & Navab, N. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528120 · Cited by 606 · see citing works
Potential liability for physicians using artificial intelligence
Price, W. N., Gerke, S., & Cohen, I. G. (2019) · JAMA · doi:10.1001/jama.2019.15064 · Cited by 579 · see citing works
Cruz-Roa, Á., Gilmore, H. L., Basavanhally, A. N., Feldman, M. D., Ganesan, S., Shih, N. N. C., Tomaszewski, J., Gonzalez, F. A., & Madabhushi, A. (2017) · Scientific Reports · doi:10.1038/srep46450 · Cited by 530 · see citing works
Accountability of AI Under the Law: The Role of Explanation
Doshi-Velez, F., Kortz, M., Budish, R., Bavitz, C., Gershman, S. J., O'Brien, D., Scott, K., Shieber, S., Waldo, J., Weinberger, D., et al. (2017) · doi:10.2139/ssrn.3064761 · Cited by 457 · see citing works
Real Time Image Saliency for Black Box ClassifiersPreprint
Dabkowski, P., & Gal, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.07857 · Cited by 404 · see citing works
Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning
Zhou, L.-Q., Wu, X.-L., Huang, S.-Y., Wu, G.-G., Ye, H.-R., Wei, Q., Bao, L.-Y., Deng, Y.-B., Li, X.-R., Cui, X.-W., et al. (2020) · Radiology · doi:10.1148/radiol.2019190372 · Cited by 384 · see citing works
A deep learning framework for supporting the classification of breast lesions in ultrasound images
Han, S., Kang, H.-K., Jeong, J.-Y., Park, M.-H., Kim, W., Bang, W.-C., & Seong, Y.-K. (2017) · Physics in Medicine & Biology · doi:10.1088/1361-6560/aa82ec · Cited by 358 · see citing works
Otto, S. J., Fracheboud, J., Looman, C. W. N., Broeders, M. J. M., Boer, R., Hendriks, J. H. C. L., Verbeek, A. L. M., & de Koning, H. J. (2003) · The Lancet · doi:10.1016/s0140-6736(03)13132-7 · Cited by 331 · see citing works
A deep learning method for classifying mammographic breast density categories
Mohamed, A. A., Berg, W. A., Peng, H., Luo, Y., Jankowitz, R. C., & Wu, S. (2017) · Medical Physics · doi:10.1002/mp.12683 · Cited by 266 · see citing works
Omeiza, D., Speakman, S., Cintas, C., & Weldermariam, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.01224 · Cited by 249 · see citing works
A Deep Learning Model to Triage Screening Mammograms: A Simulation Study
Yala, A., Schuster, T., Miles, R., Barzilay, R., & Lehman, C. (2019) · Radiology · doi:10.1148/radiol.2019182908 · Cited by 210 · see citing works
Artificial Intelligence & Human Rights: Opportunities & Risks
Raso, F., Hilligoss, H., Krishnamurthy, V., Bavitz, C., & Kim, L. Y. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3259344 · Cited by 189 · see citing works
Cao, Z., Duan, L., Yang, G., Yue, T., & Chen, Q. (2019) · BMC Medical Imaging · doi:10.1186/s12880-019-0349-x · Cited by 179 · see citing works
Burt, J. R., Torosdagli, N., Khosravan, N., RaviPrakash, H., Mortazi, A., Tissavirasingham, F., Hussein, S., & Bağcı, U. (2018) · British Journal of Radiology · doi:10.1259/bjr.20170545 · Cited by 171 · see citing works
Local Interpretable Model-Agnostic Explanations For Music Content Analysis.
Mishra, S., Sturm, B. L. T., & Dixon, S. (2017) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1417386 · Cited by 128 · see citing works
Ciritsis, A., Rossi, C., Eberhard, M., Marcon, M., Becker, A. S., & Boss, A. (2019) · European Radiology · doi:10.1007/s00330-019-06118-7 · Cited by 117 · see citing works
Breast ultrasound lesions recognition: end-to-end deep learning approaches
Yap, M. H., Goyal, M., Osman, F. M., Martí, R., Denton, E., Juette, A., & Reyer Zwiggelaar, A. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.6.1.011007 · Cited by 105 · see citing works
Qiu, Y., Wang, Y., Yan, S., Tan, M., Cheng, S. S. M., Liu, H., & Zheng, B. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2216275 · Cited by 68 · see citing works
Ultrasound screening of breast cancer
Tohno, E., Ueno, E., & Watanabe, H. (2008) · Breast Cancer · doi:10.1007/s12282-008-0082-8 · Cited by 66 · see citing works
Breast Tumor Detection in Ultrasound Images Using Deep Learning
Cao, Z., Duan, L., Yang, G., Yue, T., Chen, Q., Fu, H., & Xu, Y. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67434-6_14 · Cited by 60 · see citing works
Moss, S. M., Summerley, M. E., Thomas, B., Ellman, R., & Chamberlain, J. (1992) · Journal of Epidemiology & Community Health · doi:10.1136/jech.46.4.362 · Cited by 54 · see citing works
Artificial Intelligence for Breast MRI in 2008–2018: A Systematic Mapping Review
Codari, M., Schiaffino, S., Sardanelli, F., & Trimboli, R. M. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20389 · Cited by 54 · see citing works
Cho, K. R., Seo, B. K., Kim, C. H., Whang, K. W., Kim, Y. H., Kim, B. H., Woo, O. H., Lee, Y. H., & Chung, K. B. (2008) · Yonsei Medical Journal · doi:10.3349/ymj.2008.49.1.103 · Cited by 38 · see citing works
Osako, T., Takahashi, K., Iwase, T., Iijima, K., Miyagi, Y., Nishimura, S., Tada, K., Makita, M., Akiyama, F., Sakamoto, G., et al. (2007) · Breast Cancer · doi:10.2325/jbcs.891 · Cited by 37 · see citing works
Deep Learning Model to Assess Cancer Risk on the Basis of a Breast MR Image Alone
Portnoi, T., Yala, A., Schuster, T., Barzilay, R., Dontchos, B. N., Lamb, L. R., & Lehman, C. D. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20813 · Cited by 37 · see citing works
Su, X., Lin, Q., Cui, C., Xu, W., Wei, Z., Fei, J., & Li, L. (2016) · Breast Cancer · doi:10.1007/s12282-016-0739-7 · Cited by 36 · see citing works
Self-citationJin, Z., Lin, M.-Y., Hao, W.-Q., Jiang, H.-T., Zhang, L., Hu, W.-H., & Zhang, M. (2014) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2014.09.023 · Cited by 34 · see citing works
Breast Lesion Segmentation in Ultrasound Images with Limited Annotated Data
Behboodi, B., Amiri, M., Brooks, R., & Rivaz, H. J. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098685 · Cited by 15 · see citing works
Artificial Intelligence and Automated Law Enforcement: A Review PaperPreprint
Petit, N. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3145133 · Cited by 12 · see citing works
The benefits and harms of breast cancer screening
Donzelli, A. (2013) · The Lancet · doi:10.1016/s0140-6736(13)60619-4 · Cited by 10 · see citing works
Can Machine Learning Be Used to Generate a Model to Improve Management of High-Risk Breast Lesions?
Shaffer, K. (2018) · Radiology · doi:10.1148/radiol.2017172648 · Cited by 7 · see citing works
Asymptotical Optimality of Sequential Universal Hypothesis Testing Based on the Method of Types
Xu, Y., & Wang, Q. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2014.2333562 · Cited by 4 · see citing works
Flagged first, then by citations
1 of the 56 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Shen, Y., Shamout, F. E., Oliver, J. R., Witowski, J. S., Kannan, K., Park, J., Wu, N., Huddleston, C., Wolfson, S., Millet, A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26023-2 · Cited by 278 · see citing works
Champendal, M., Müller, H., Prior, J. O., & dos Reis, C. S. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.111159 · Cited by 94 · see citing works
Groen, A. M., Kraan, R., Amirkhan, S. F., Daams, J. G., & Maas, M. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110592 · Cited by 89 · see citing works
Gurmessa, D. K., & Jimma, W. (2024) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2023-100954 · Cited by 74 · see citing works
Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis
Li, Y., Li, Q., Fu, K., Zhou, X., & Zhang, K. (2025) · Bioengineering · doi:10.3390/bioengineering12030288 · Cited by 71 · see citing works
Song, D., Yao, J., Jiang, Y., Shi, S., Cui, C., Wang, L., Wang, L., Wu, H., Tian, H., Ye, X., et al. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107527 · Cited by 60 · see citing works
Self-citationCerekci, E. A., Alis, D. C., Denizoğlu, N., Camurdan, O., Şeker, M. E., Özer, C., Hansu, M. Y., Tanyel, T., Öksüz, İ., & Karaarslan, E. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111356 · Cited by 55 · see citing works
Artificial intelligence in breast imaging: Current situation and clinical challenges
You, C., Shen, Y., Sun, S., Zhou, J., Li, J., Su, G.-H., Michalopoulou, E., Peng, W., Gu, Y., Guo, W., et al. (2023) · Exploration · doi:10.1002/exp.20230007 · Cited by 45 · see citing works
Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., & Recker, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216833 · Cited by 44 · see citing works
Meraj, T., Alosaimi, W. A., Alouffi, B., Rauf, H. T., Kumar, S. A., Damaševičius, R., & Alyami, H. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.805 · Cited by 44 · see citing works
Rainey, C., O’Regan, T., Matthew, J., Skelton, E., Woznitza, N., Chu, K.-Y., Goodman, S., McConnell, J., Hughes, C., Bond, R., et al. (2022) · Radiography · doi:10.1016/j.radi.2022.06.006 · Cited by 42 · see citing works
Ultrasound radiomics in personalized breast management: Current status and future prospects
Gu, J., & Jiang, T. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.963612 · Cited by 37 · see citing works
Zhang, W., Wang, S., Wang, Y., Sun, J., Wei, H., Xue, W., Xueying, D., & Wang, X. (2024) · La radiologia medica · doi:10.1007/s11547-024-01768-0 · Cited by 36 · see citing works
Gu, B., Meng, M., Bi, L., Kim, J., Feng, D. D., & Song, S. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.899351 · Cited by 28 · see citing works
Artificial intelligence for hepatitis evaluation
Liu, W., Liu, X., Peng, M., Chen, G.-Q., Liu, P.-H., Cui, X., Jiang, F., & Dietrich, C. F. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i34.5715 · Cited by 26 · see citing works
Wang, S., Cao, C., Du, T., Wang, J., Li, J., Li, W., & Chen, M. (2024) · Journal of Ultrasound in Medicine · doi:10.1002/jum.16483 · Cited by 23 · see citing works
Nicosia, L., Pesapane, F., Bozzini, A. C., Latronico, A., Rotili, A., Ferrari, F., Signorelli, G., Raimondi, S., Vignati, S., Gaeta, A., et al. (2023) · Cancers · doi:10.3390/cancers15030964 · Cited by 21 · see citing works
Wyatt, L. S., van Karnenbeek, L. M., Wijkhuizen, M., Geldof, F., & Dashtbozorg, B. (2024) · Applied Sciences · doi:10.3390/app14188108 · Cited by 19 · see citing works
Himel, M. H. A. M. H., Chowdhury, P., & Hasan, M. A. M. (2024) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2024.200367 · Cited by 17 · see citing works
Bartolotta, T. V., Militello, C., Prinzi, F., Ferraro, F., Rundo, L., Zarcaro, C., Dimarco, M., Orlando, A. A. M., Matranga, D., & Vitabile, S. (2024) · La radiologia medica · doi:10.1007/s11547-024-01826-7 · Cited by 15 · see citing works
Detection and Classification of Breast Lesions with You Only Look Once Version 5
Meng, M., Zhang, M., Shen, D., He, G., & Guo, Y. (2022) · Future Oncology · doi:10.2217/fon-2022-0593 · Cited by 13 · see citing works
Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet
Wang, L., Zhang, M., He, G., Shen, D., & Meng, M. (2023) · Diagnostics · doi:10.3390/diagnostics13061067 · Cited by 13 · see citing works
Artificial Intelligence for Breast Ultrasound: Will It Impact Radiologists’ Accuracy?
Bahl, M. (2021) · Journal of Breast Imaging · doi:10.1093/jbi/wbab022 · Cited by 10 · see citing works
Hu, W., Li, X., Tang, Y., Mao, X., Li, C., Wang, Y., Li, Z., Zuo, M., Yin, L., Deng, Y., et al. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01982-w · Cited by 9 · see citing works
Wu, H., Ye, X., Jiang, Y., Tian, H., Yang, K., Cui, C., Shi, S., Liu, Y., Huang, S., Chen, J., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.869421 · Cited by 8 · see citing works
Self-citationFatima, M., Zia, R., & Usmani, I. A. (2025) · Systems and Soft Computing · doi:10.1016/j.sasc.2025.200412 · Cited by 7 · see citing works
Wang, S., Tian, F., Zhu, T., Cao, C.-L., Wang, J.-L., Li, W., Li, J., & Hou, J. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1548888 · Cited by 5 · see citing works
Interpretability of Machine Learning Models for Breast Cancer Identification: A Review
Ahmad, I., Amelio, A., Gernsback, D. H., Merla, A., & Scozzari, F. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-7419-7_17 · Cited by 3 · see citing works
Huang, Z., Li, G., Wang, M., Mo, S., Wu, H., Tian, H., Tang, S., Xu, J., & Dong, F. (2025) · Photoacoustics · doi:10.1016/j.pacs.2025.100764 · Cited by 3 · see citing works
Self-citationAn unsupervised XAI framework for dementia detection with context enrichment
Singh, D. S., Brima, Y., Levin, F., Becker, M., Hiller, B. C., Hermann, A., Villar-Munoz, I., Beichert, L., Bernhardt, A., Büerger, K., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-26227-2 · Cited by 3 · see citing works
Li, L., Pan, C., Zhang, M., Shen, D., He, G., & Meng, M. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01484-1 · Cited by 3 · see citing works
Raheem, A., Yang, Z., Alluhaidan, A. S., Manan, M. A., Ahmed, S., Sabah, F., & Ahmad, S. (2025) · Digital Health · doi:10.1177/20552076251390564 · Cited by 2 · see citing works
Huang, Z., Wang, M., Kong, Y., Li, G., Tian, H., Wu, H., Zheng, J., Mo, S., Xu, J., & Dong, F. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.10.036 · Cited by 2 · see citing works
Self-citationHuang, Z., Wang, M., Tian, H., Li, G., Wu, H., Chen, J., Kong, Y., Mo, S., Tang, S., Yin, Y., et al. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.10.018 · Cited by 2 · see citing works
Self-citationBong, J. H., Kim, T. H., & Jeong, S. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-22-880 · Cited by 2 · see citing works
Fawzy, A. E., Almandouh, M. E., Herajy, M., & Eisa, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-38218-y · Cited by 1 · see citing works
Zhu, M., Xu, Z., Hu, J. Z., Hua, L., Zou, Y., Qin, F., & Chen, C. (2024) · World Journal of Surgical Oncology · doi:10.1186/s12957-024-03498-z · Cited by 1 · see citing works
Jiang, Y., Wu, H., Cui, C., Shi, S., Ye, X., Tian, H., Song, D., Yao, J., Wang, L., Zhang, M., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4185034
Self-citationChen, J., Wu, H., Jiang, Y., Tang, S., Cui, C., Shi, S., Tian, H., Yang, K., huang, Z., Xu, J., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3296423/v1
Self-citationResearch on Ultrasound Image Based Prostate Cancer Auxiliary Diagnosis System
Li, X., Liu, Z., & Yang, L. (2024) · Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) · doi:10.1109/ipec61310.2024.00101
李, 禄. (2025) · Advances in Clinical Medicine · doi:10.12677/acm.2025.152503
Medikal Görüntüleme ve Yapay Zekâ
Süreyya, N. U. R. (2025) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1104.c4419
Metacognitive efficiency in learned value-based choice
Ershadmanesh, S., Gholamzadeh, A., Desender, K., & Dayan, P. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014108
- Authors
- Dong, F., She, R., Cui, C., Shi, S., Hu, X., Zeng, J., Wu, H., Xu, J., & Zhang, Y.
- Affiliations
- Shandong University (CN)
- Jinan University (CN)
- Corresponding author
- Xu, Jinfeng
- Zhang, Yun
- Venue
- European Radiology
- Published
- 2021-01-06
- Volume
- 31
- Issue
- 7
- Pages
- 4991-5000
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- OpenAlex
- W3119612737
- Cited by
- 43
- References
- 56
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 12 · 2023: 8 · 2022: 7 · 2021: 4
- FWCI
- 2.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- European Radiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medical Science and Technology Research Foundation of Guangdong (B2019045)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Pathology and Forensic Medicine › Breast Lesions and Carcinomas
- Keywords
- breast ultrasound, explainable AI, DenseNet121, deep learning, benign versus malignant breast lesions, transparency in decision-making
- MeSH terms
- Artificial Intelligence, Breast, Breast Neoplasms, Female, Humans, Pilot Projects, Sensitivity and Specificity, Ultrasonography
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
