# Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network

arXiv: https://arxiv.org/abs/2110.01606 · HTML: https://mlsanity.com/paper/2110.01606/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation](https://mlsanity.com/paper/1810.13243/) (2018)
- [Accelerating Very Deep Convolutional Networks for Classification and Detection](https://mlsanity.com/paper/1505.06798/) (2015)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design](https://mlsanity.com/paper/1711.05775/) (2017)
- [Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations](https://mlsanity.com/paper/2101.07945/) (2021)

## Built on by (most cited first, 13 of 13)

- [Towards Generalist Biomedical AI](https://mlsanity.com/paper/2307.14334/) (2023)
- [Multi-View Hypercomplex Learning for Breast Cancer Screening](https://mlsanity.com/paper/2204.05798/) (2022)
- [Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram Classification](https://mlsanity.com/paper/2306.10676/) (2023)
- [Case-level Breast Cancer Prediction for Real Hospital Settings](https://mlsanity.com/paper/2310.12677/) (2023)
- [Bridging Contrastive Learning and Domain Adaptation: Theoretical Perspective and Practical Application](https://mlsanity.com/paper/2502.00052/) (2025)
- [Optimizing Breast Cancer Detection in Mammograms: A Comprehensive Study of Transfer Learning, Resolution Reduction, and Multi-View Classification](https://mlsanity.com/paper/2503.19945/) (2025)
- [Bidirectional Mammogram View Translation with Column-Aware and Implicit 3D Conditional Diffusion](https://mlsanity.com/paper/2510.04947/) (2025)
- [Interpretable breast cancer classification using CNNs on mammographic images](https://mlsanity.com/paper/2408.13154/) (2024)
- [Hybrid Deep Learning and Handcrafted Feature Fusion for Mammographic Breast Cancer Classification](https://mlsanity.com/paper/2507.19843/) (2025)
- [Evaluating Federated Learning approaches for mammography under breast density heterogeneity](https://mlsanity.com/paper/2605.09137/) (2026)
- [MammoFlow: Multiview Mammogram Synthesis with Anatomically Consistent Flow Matching](https://mlsanity.com/paper/2606.28537/) (2026)
- [Token-Based Dual-view Fusion and Adaptation of Large Vision Models for Breast Cancer Classification](https://mlsanity.com/paper/2607.06309/) (2026)
- [Lesion-Aware Attention for Mammography Segmentation with Heterogeneous Supervision](https://mlsanity.com/paper/2610.15612/) (2026)

## Abstract (arXiv)

Some recent studies have described deep convolutional neural networks to diagnose breast cancer in mammograms with similar or even superior performance to that of human experts. One of the best techniques does two transfer learnings: the first uses a model trained on natural images to create a "patch classifier" that categorizes small subimages; the second uses the patch classifier to scan the whole mammogram and create the "single-view whole-image classifier". We propose to make a third transfer learning to obtain a "two-view classifier" to use the two mammographic views: bilateral craniocaudal and mediolateral oblique. We use EfficientNet as the basis of our model. We "end-to-end" train the entire system using CBIS-DDSM dataset. To ensure statistical robustness, we test our system twice using: (a) 5-fold cross validation; and (b) the original training/test division of the dataset. Our technique reached an AUC of 0.9344 using 5-fold cross validation (accuracy, sensitivity and specificity are 85.13% at the equal error rate point of ROC). Using the original dataset division, our technique achieved an AUC of 0.8483, as far as we know the highest reported AUC for this problem, although the subtle differences in the testing conditions of each work do not allow for an accurate comparison. The inference code and model are available at https://github.com/dpetrini/two-views-classifier