mlsanityData to 7 Oct 2026

Detecting and classifying lesions in mammograms with Deep Learning

arXiv 1707.08401 ↗First posted 26 Jul 2017cs.CVcited by 47

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Abstract (arXiv)

In the last two decades Computer Aided Diagnostics (CAD) systems were developed to help radiologists analyze screening mammograms. The benefits of current CAD technologies appear to be contradictory and they should be improved to be ultimately considered useful. Since 2012 deep convolutional neural networks (CNN) have been a tremendous success in image recognition, reaching human performance. These methods have greatly surpassed the traditional approaches, which are similar to currently used CAD solutions. Deep CNN-s have the potential to revolutionize medical image analysis. We propose a CAD system based on one of the most successful object detection frameworks, Faster R-CNN. The system detects and classifies malignant or benign lesions on a mammogram without any human intervention. The proposed method sets the state of the art classification performance on the public INbreast database, AUC = 0.95 . The approach described here has achieved the 2nd place in the Digital Mammography DREAM Challenge with AUC = 0.85 . When used as a detector, the system reaches high sensitivity with very few false positive marks per image on the INbreast dataset. Source code, the trained model and an OsiriX plugin are availaible online at https://github.com/riblidezso/frcnn_cad .

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jul 2017.

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Citing papers per year: 2021: 12; 2022: 10; 2023: 13; 2024: 6; 2025: 6.

47 papers.

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  1. 2024BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at oncecited by 107
  2. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  3. 2023CLIP in Medical Imaging: A Surveycited by 40
  4. 2024A survey of synthetic data augmentation methods in computer visioncited by 34
  5. 2022Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imagingcited by 32
  6. 2021Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detectioncited by 22
  7. 2021Memory-aware curriculum federated learning for breast cancer classificationcited by 20
  8. 2023DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Countingcited by 18
  9. 2022Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learningcited by 16
  10. 2022Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomaliescited by 14
  11. 2022Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisationcited by 12
  12. 2022Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center studycited by 10
35 more
  1. 2023Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directionscited by 10
  2. 2024FocusMAE: Gallbladder Cancer Detection from Ultrasound Videos with Focused Masked Autoencoderscited by 8
  3. 2021Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosiscited by 7
  4. 2021Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformationscited by 5
  5. 2021Meta-repository of screening mammography classifierscited by 4
  6. 2021Multi-task fusion for improving mammography screening data classificationcited by 4
  7. 2022Check and Link: Pairwise Lesion Correspondence Guides Mammogram Mass Detectioncited by 4
  8. 2023RADIFUSION: A multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinementcited by 4
  9. 2021Artificial Intelligence For Breast Cancer Detection: Trends & Directionscited by 3
  10. 2023Harnessing Collective Intelligence Under a Lack of Cultural Consensuscited by 2
  11. 2023TransReg: Cross-transformer as auto-registration module for multi-view mammogram mass detectioncited by 2
  12. 2021Breast Mass Detection with Faster R-CNN: On the Feasibility of Learning from Noisy Annotationscited by 1
  13. 2021Multimodal Breast Lesion Classification Using Cross-Attention Deep Networkscited by 1
  14. 2021A survey on deep learning approaches for breast cancer diagnosiscited by 1
  15. 2022DKMA-ULD: Domain Knowledge augmented Multi-head Attention based Robust Universal Lesion Detectioncited by 1
  16. 2022An Interactive Interpretability System for Breast Cancer Screening with Deep Learningcited by 1
  17. 2023Improving Mass Detection in Mammography Images: A Study of Weakly Supervised Learning and Class Activation Map Methodscited by 1
  18. 2023Case-level Breast Cancer Prediction for Real Hospital Settingscited by 1
  19. 2023Deep Learning as a Method for Inversion of NMR Signalscited by 1
  20. 2024LQ-Adapter: ViT-Adapter with Learnable Queries for Gallbladder Cancer Detection from Ultrasound Imagecited by 1
  21. 2025Enhancing breast cancer detection on screening mammogram using self-supervised learning and a hybrid deep model of Swin Transformer and Convolutional Neural Networkcited by 1
  22. 2021Deep Learning for Breast Cancer Classification: Enhanced Tangent Functioncited by 0
  23. 2022A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray Images using Convolutional Neural Networkscited by 0
  24. 2022Independent evaluation of state-of-the-art deep networks for mammographycited by 0
  25. 2023EfficientNet Algorithm for Classification of Different Types of Cancercited by 0
  26. 2023Multivariate Analysis on Performance Gaps of Artificial Intelligence Models in Screening Mammographycited by 0
  27. 2023Localisation of Mammographic masses by Greedy Backtracking of Activations in the Stacked Auto-Encoderscited by 0
  28. 2023Unsupversied feature correlation model to predict breast abnormal variation maps in longitudinal mammogramscited by 0
  29. 2024Deep Learning Innovations in Diagnosing Diabetic Retinopathy: The Potential of Transfer Learning and the DiaCNN Modelcited by 0
  30. 2024A training regime to learn unified representations from complementary breast imaging modalitiescited by 0
  31. 2025Mesters'eges Intelligencia Kutat'asok Magyarorsz'agoncited by 0
  32. 2025Efficient Multi-scale Masked Autoencoders with Hybrid-Attention Mechanism for Breast Lesion Classificationcited by 0
  33. 2025Cross-Attention Multimodal Fusion for Breast Cancer Diagnosis: Integrating Mammography and Clinical Data with Explainabilitycited by 0
  34. 2025MetaChest: Generalized few-shot learning of pathologies from chest X-rayscited by 0
  35. 2025Topological Conditioning for Mammography Models via a Stable Wavelet-Persistence Vectorizationcited by 0