# Multi-Attention Multiple Instance Learning

arXiv: https://arxiv.org/abs/2112.06071 · HTML: https://mlsanity.com/paper/2112.06071/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 67 references that are in the map.
- First posted on arXiv 11 Dec 2021.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis](https://mlsanity.com/paper/1804.06353/) (2018)
- [Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks](https://mlsanity.com/paper/2009.11169/) (2020)
- [Deep neural network models for computational histopathology: A survey](https://mlsanity.com/paper/1912.12378/) (2019)
- [Multiple Instance Learning: A Survey of Problem Characteristics and Applications](https://mlsanity.com/paper/1612.03365/) (2016)
- [An Attentive Survey of Attention Models](https://mlsanity.com/paper/1904.02874/) (2019)
- [Revisiting Multiple Instance Neural Networks](https://mlsanity.com/paper/1610.02501/) (2016)
- [Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond](https://mlsanity.com/paper/2103.10689/) (2021)
- [Dive into Deep Learning](https://mlsanity.com/paper/2106.11342/) (2021)
- [Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning](https://mlsanity.com/paper/1511.05286/) (2015)
- [Resolving challenges in deep learning-based analyses of histopathological images using explanation methods](https://mlsanity.com/paper/1908.06943/) (2019)
- [MHAttnSurv: Multi-Head Attention for Survival Prediction Using Whole-Slide Pathology Images](https://mlsanity.com/paper/2110.11558/) (2021)
- [Nested Multiple Instance Learning with Attention Mechanisms](https://mlsanity.com/paper/2111.00947/) (2021)
- [Multiple Instance Learning Convolutional Neural Networks for Object Recognition](https://mlsanity.com/paper/1610.03155/) (2016)

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

- [Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images](https://mlsanity.com/paper/2203.07707/) (2022)
- [Towards Hierarchical Regional Transformer-based Multiple Instance Learning](https://mlsanity.com/paper/2308.12634/) (2023)
- [Interpreting Vulnerabilities of Multi-Instance Learning to Adversarial Perturbations](https://mlsanity.com/paper/2211.17071/) (2022)
- [Data-driven Knowledge Fusion for Deep Multi-instance Learning](https://mlsanity.com/paper/2304.11905/) (2023)
- [ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging](https://mlsanity.com/paper/2306.10535/) (2023)
- [Multiple Instance Learning with Trainable Decision Tree Ensembles](https://mlsanity.com/paper/2302.06601/) (2023)

## Abstract (arXiv)

A new multi-attention based method for solving the MIL problem (MAMIL), which takes into account the neighboring patches or instances of each analyzed patch in a bag, is proposed. In the method, one of the attention modules takes into account adjacent patches or instances, several attention modules are used to get a diverse feature representation of patches, and one attention module is used to unite different feature representations to provide an accurate classification of each patch (instance) and the whole bag. Due to MAMIL, a combined representation of patches and their neighbors in the form of embeddings of a small dimensionality for simple classification is realized. Moreover, different types of patches are efficiently processed, and a diverse feature representation of patches in a bag by using several attention modules is implemented. A simple approach for explaining the classification predictions of patches is proposed. Numerical experiments with various datasets illustrate the proposed method.