# Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

arXiv: https://arxiv.org/abs/1612.05968 · HTML: https://mlsanity.com/paper/1612.05968/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2016.

## Built on by (most cited first, 30 of 32)

- [DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2203.12081/) (2022)
- [Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides](https://mlsanity.com/paper/2112.02222/) (2021)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology](https://mlsanity.com/paper/2406.04280/) (2024)
- [Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies](https://mlsanity.com/paper/2203.13716/) (2022)
- [Shapley Values-enabled Progressive Pseudo Bag Augmentation for Whole Slide Image Classification](https://mlsanity.com/paper/2312.05490/) (2023)
- [Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models](https://mlsanity.com/paper/2209.12420/) (2022)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study](https://mlsanity.com/paper/2201.11620/) (2022)
- [Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation](https://mlsanity.com/paper/2210.05946/) (2022)
- [Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis](https://mlsanity.com/paper/2106.07049/) (2021)
- [EGFR Mutation Prediction of Lung Biopsy Images using Deep Learning](https://mlsanity.com/paper/2208.12506/) (2022)
- [MammoDG: Generalisable Deep Learning Breaks the Limits of Cross-Domain Multi-Center Breast Cancer Screening](https://mlsanity.com/paper/2308.01057/) (2023)
- [All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification](https://mlsanity.com/paper/2205.03371/) (2022)
- [SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2311.00048/) (2023)
- [Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram Classification](https://mlsanity.com/paper/2306.10676/) (2023)
- [Fast and Accurate Gigapixel Pathological Image Classification with Hierarchical Distillation Multi-Instance Learning](https://mlsanity.com/paper/2502.21130/) (2025)
- [Multi-task fusion for improving mammography screening data classification](https://mlsanity.com/paper/2112.01320/) (2021)
- [Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification](https://mlsanity.com/paper/2305.02032/) (2023)
- [VALD-MD: Visual Attribution via Latent Diffusion for Medical Diagnostics](https://mlsanity.com/paper/2401.01414/) (2024)
- [Continual Multiple Instance Learning with Enhanced Localization for Histopathological Whole Slide Image Analysis](https://mlsanity.com/paper/2507.02395/) (2025)
- [Improving Mass Detection in Mammography Images: A Study of Weakly Supervised Learning and Class Activation Map Methods](https://mlsanity.com/paper/2308.03486/) (2023)
- [Case-level Breast Cancer Prediction for Real Hospital Settings](https://mlsanity.com/paper/2310.12677/) (2023)
- [Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images](https://mlsanity.com/paper/2310.19359/) (2023)
- [Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals](https://mlsanity.com/paper/2406.05477/) (2024)
- [Synthetic Data Reveals Generalization Gaps in Correlated Multiple Instance Learning](https://mlsanity.com/paper/2510.25759/) (2025)
- [A Multi-Dataset Benchmark of Multiple Instance Learning for 3D Neuroimage Classification](https://mlsanity.com/paper/2604.26807/) (2026)
- [Multiple Instance Learning for Uplift Modeling](https://mlsanity.com/paper/2312.09639/) (2023)
- [Evaluating Federated Learning approaches for mammography under breast density heterogeneity](https://mlsanity.com/paper/2605.09137/) (2026)
- [Clinically-Grounded Counterfactual Reasoning for Medical Video Diagnosis](https://mlsanity.com/paper/2605.26483/) (2026)

## Abstract (arXiv)

Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods requires great effort to annotate the training data by costly manual labeling and specialized computational models to detect these annotations during test. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed deep networks compared to previous work using segmentation and detection annotations in the training.