# Multi-head Attention-based Deep Multiple Instance Learning

arXiv: https://arxiv.org/abs/2404.05362 · HTML: https://mlsanity.com/paper/2404.05362/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2024.

## Built on by (most cited first, 6 of 6)

- [Biochemical Prostate Cancer Recurrence Prediction: Thinking Fast & Slow](https://mlsanity.com/paper/2409.02284/) (2024)
- [Beyond accuracy: quantifying the reliability of Multiple Instance Learning for Whole Slide Image classification](https://mlsanity.com/paper/2409.11110/) (2024)
- [Synthetic Data Reveals Generalization Gaps in Correlated Multiple Instance Learning](https://mlsanity.com/paper/2510.25759/) (2025)
- [Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification](https://mlsanity.com/paper/2503.10510/) (2025)
- [Learning from the Right Patches: A Two-Stage Wavelet-Driven Masked Autoencoder for Histopathology Representation Learning](https://mlsanity.com/paper/2511.06958/) (2025)
- [Normal Guidance is what Attention Needs](https://mlsanity.com/paper/2605.27306/) (2026)

## Abstract (arXiv)

This paper introduces MAD-MIL, a Multi-head Attention-based Deep Multiple Instance Learning model, designed for weakly supervised Whole Slide Images (WSIs) classification in digital pathology. Inspired by the multi-head attention mechanism of the Transformer, MAD-MIL simplifies model complexity while achieving competitive results against advanced models like CLAM and DS-MIL. Evaluated on the MNIST-BAGS and public datasets, including TUPAC16, TCGA BRCA, TCGA LUNG, and TCGA KIDNEY, MAD-MIL consistently outperforms ABMIL. This demonstrates enhanced information diversity, interpretability, and efficiency in slide representation. The model's effectiveness, coupled with fewer trainable parameters and lower computational complexity makes it a promising solution for automated pathology workflows. Our code is available at https://github.com/tueimage/MAD-MIL.