CC BY-NC-ND 4.0 · Indian J Radiol Imaging 2024; 34(03): 469-487
DOI: 10.1055/s-0043-1775737
Systematic Review

Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature

Deeksha Bhalla
1   Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India
Krithika Rangarajan
1   Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India
Tany Chandra
1   Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India
Subhashis Banerjee
2   Department of Computer Science and Engineering, Indian Institute of Technology, New Delhi, India
Chetan Arora
2   Department of Computer Science and Engineering, Indian Institute of Technology, New Delhi, India
› Author Affiliations
Funding This work was supported in part by the Department of Biotechnology, Government of India, under grant BT/PR33193/AI/133/5/2019.


Background Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely variable.

Purpose To evaluate published literature on breast cancer detection in mammography for reproducibility and to ascertain best practices for model design.

Methods The PubMed and Scopus databases were searched to identify records that described the use of deep learning to detect lesions or classify images into cancer or noncancer. A modification of Quality Assessment of Diagnostic Accuracy Studies (mQUADAS-2) tool was developed for this review and was applied to the included studies. Results of reported studies (area under curve [AUC] of receiver operator curve [ROC] curve, sensitivity, specificity) were recorded.

Results A total of 12,123 records were screened, of which 107 fit the inclusion criteria. Training and test datasets, key idea behind model architecture, and results were recorded for these studies. Based on mQUADAS-2 assessment, 103 studies had high risk of bias due to nonrepresentative patient selection. Four studies were of adequate quality, of which three trained their own model, and one used a commercial network. Ensemble models were used in two of these. Common strategies used for model training included patch classifiers, image classification networks (ResNet in 67%), and object detection networks (RetinaNet in 67%). The highest reported AUC was 0.927 ± 0.008 on a screening dataset, while it reached 0.945 (0.919–0.968) on an enriched subset. Higher values of AUC (0.955) and specificity (98.5%) were reached when combined radiologist and Artificial Intelligence readings were used than either of them alone. None of the studies provided explainability beyond localization accuracy. None of the studies have studied interaction between AI and radiologist in a real world setting.

Conclusion While deep learning holds much promise in mammography interpretation, evaluation in a reproducible clinical setting and explainable networks are the need of the hour.

Supplementary Material

Publication History

Article published online:
10 October 2023

© 2023. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (

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