Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks
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Abstract (arXiv)
Mammography is the most widely used method to screen breast cancer. Because of its mostly manual nature, variability in mass appearance, and low signal-to-noise ratio, a significant number of breast masses are missed or misdiagnosed. In this work, we present how Convolutional Neural Networks can be used to directly classify pre-segmented breast masses in mammograms as benign or malignant, using a combination of transfer learning, careful pre-processing and data augmentation to overcome limited training data. We achieve state-of-the-art results on the DDSM dataset, surpassing human performance, and show interpretability of our model.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Dec 2016.
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Citing papers per year: 2021: 4; 2022: 3; 2023: 3; 2024: 2.
12 papers.
- 2023Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedbackcited by 14
- 2022Improving the repeatability of deep learning models with Monte Carlo dropoutcited by 11
- 2021Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformationscited by 5
- 2021A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Ricacited by 3
- 2022Pavementscapes: a large-scale hierarchical image dataset for asphalt pavement damage segmentationcited by 2
- 2023TransReg: Cross-transformer as auto-registration module for multi-view mammogram mass detectioncited by 2
- 2021Breast Mass Detection with Faster R-CNN: On the Feasibility of Learning from Noisy Annotationscited by 1
- 2023Case-level Breast Cancer Prediction for Real Hospital Settingscited by 1
- 2021Use of Transfer Learning and Wavelet Transform for Breast Cancer Detectioncited by 0
- 2022A Sneak Attack on Segmentation of Medical Images Using Deep Neural Network Classifierscited by 0
- 2024Stacking-Enhanced Bagging Ensemble Learning for Breast Cancer Classification with CNNcited by 0
- 2024Computer Aided Detection and Classification of mammograms using Convolutional Neural Networkcited by 0
- 2024Computer Aided Detection and Classification of mammograms using Convolutional Neural Network4 Sep 2024
- 2024Stacking-Enhanced Bagging Ensemble Learning for Breast Cancer Classification with CNN15 Jul 2024
- 2023TransReg: Cross-transformer as auto-registration module for multi-view mammogram mass detection9 Nov 2023
- 2023Case-level Breast Cancer Prediction for Real Hospital Settings19 Oct 2023
- 2023Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedback16 Jun 2023
- 2022Pavementscapes: a large-scale hierarchical image dataset for asphalt pavement damage segmentation24 Jul 2022
- 2022Improving the repeatability of deep learning models with Monte Carlo dropout15 Feb 2022
- 2022A Sneak Attack on Segmentation of Medical Images Using Deep Neural Network Classifiers8 Jan 2022
- 2021A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica24 Jul 2021
- 2021Breast Mass Detection with Faster R-CNN: On the Feasibility of Learning from Noisy Annotations25 Apr 2021
- 2021Use of Transfer Learning and Wavelet Transform for Breast Cancer Detection5 Mar 2021
- 2021Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations20 Jan 2021