# Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis

arXiv: https://arxiv.org/abs/2106.07049 · HTML: https://mlsanity.com/paper/2106.07049/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 55 references that are in the map.
- First posted on arXiv 13 Jun 2021.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization](https://mlsanity.com/paper/1704.04232/) (2017)
- [Deep learning with noisy labels: exploring techniques and remedies in medical image analysis](https://mlsanity.com/paper/1912.02911/) (2019)
- [Adversarial Complementary Learning for Weakly Supervised Object Localization](https://mlsanity.com/paper/1804.06962/) (2018)
- [Attention-based Dropout Layer for Weakly Supervised Object Localization](https://mlsanity.com/paper/1908.10028/) (2019)
- [Evaluating Weakly Supervised Object Localization Methods Right](https://mlsanity.com/paper/2001.07437/) (2020)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [Roto-Translation Covariant Convolutional Networks for Medical Image Analysis](https://mlsanity.com/paper/1804.03393/) (2018)
- [Self-produced Guidance for Weakly-supervised Object Localization](https://mlsanity.com/paper/1807.08902/) (2018)
- [SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound](https://mlsanity.com/paper/1612.05601/) (2016)
- [Weakly Supervised Cascaded Convolutional Networks](https://mlsanity.com/paper/1611.08258/) (2016)
- [Detecting and classifying lesions in mammograms with Deep Learning](https://mlsanity.com/paper/1707.08401/) (2017)
- [An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization](https://mlsanity.com/paper/2002.07613/) (2020)
- [Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification](https://mlsanity.com/paper/1801.09927/) (2018)
- [Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification](https://mlsanity.com/paper/1612.05968/) (2016)
- [Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs](https://mlsanity.com/paper/1807.07532/) (2018)
- [Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions](https://mlsanity.com/paper/1803.07703/) (2018)
- [Attention-Gated Networks for Improving Ultrasound Scan Plane Detection](https://mlsanity.com/paper/1804.05338/) (2018)
- [Multi-label Thoracic Disease Image Classification with Cross-Attention Networks](https://mlsanity.com/paper/2007.10859/) (2020)
- [Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis](https://mlsanity.com/paper/1812.07509/) (2018)
- [Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules](https://mlsanity.com/paper/1707.01086/) (2017)
- [Deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in X-ray images](https://mlsanity.com/paper/1808.08280/) (2018)
- [Globally-Aware Multiple Instance Classifier for Breast Cancer Screening](https://mlsanity.com/paper/1906.02846/) (2019)
- [Weakly Supervised Universal Fracture Detection in Pelvic X-rays](https://mlsanity.com/paper/1909.02077/) (2019)
- [Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis](https://mlsanity.com/paper/1906.02999/) (2019)
- [Improve CAM with Auto-adapted Segmentation and Co-supervised Augmentation](https://mlsanity.com/paper/1911.07160/) (2019)

## Built on by (most cited first, 7 of 7)

- [Adaptive Early-Learning Correction for Segmentation from Noisy Annotations](https://mlsanity.com/paper/2110.03740/) (2021)
- [Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation](https://mlsanity.com/paper/2209.10478/) (2022)
- [Negative Evidence Matters in Interpretable Histology Image Classification](https://mlsanity.com/paper/2201.02445/) (2022)
- [Meta-repository of screening mammography classifiers](https://mlsanity.com/paper/2108.04800/) (2021)
- [Case-level Breast Cancer Prediction for Real Hospital Settings](https://mlsanity.com/paper/2310.12677/) (2023)
- [Independent evaluation of state-of-the-art deep networks for mammography](https://mlsanity.com/paper/2206.12407/) (2022)
- [DoSReMC: Domain Shift Resilient Mammography Classification using Batch Normalization Adaptation](https://mlsanity.com/paper/2508.15452/) (2025)

## Abstract (arXiv)

In the last few years, deep learning classifiers have shown promising results in image-based medical diagnosis. However, interpreting the outputs of these models remains a challenge. In cancer diagnosis, interpretability can be achieved by localizing the region of the input image responsible for the output, i.e. the location of a lesion. Alternatively, segmentation or detection models can be trained with pixel-wise annotations indicating the locations of malignant lesions. Unfortunately, acquiring such labels is labor-intensive and requires medical expertise. To overcome this difficulty, weakly-supervised localization can be utilized. These methods allow neural network classifiers to output saliency maps highlighting the regions of the input most relevant to the classification task (e.g. malignant lesions in mammograms) using only image-level labels (e.g. whether the patient has cancer or not) during training. When applied to high-resolution images, existing methods produce low-resolution saliency maps. This is problematic in applications in which suspicious lesions are small in relation to the image size. In this work, we introduce a novel neural network architecture to perform weakly-supervised segmentation of high-resolution images. The proposed model selects regions of interest via coarse-level localization, and then performs fine-grained segmentation of those regions. We apply this model to breast cancer diagnosis with screening mammography, and validate it on a large clinically-realistic dataset. Measured by Dice similarity score, our approach outperforms existing methods by a large margin in terms of localization performance of benign and malignant lesions, relatively improving the performance by 39.6% and 20.0%, respectively. Code and the weights of some of the models are available at https://github.com/nyukat/GLAM