# Multiple Instance Learning with random sampling for Whole Slide Image Classification

arXiv: https://arxiv.org/abs/2403.05351 · HTML: https://mlsanity.com/paper/2403.05351/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge](https://mlsanity.com/paper/1807.08284/) (2018)
- [Multimodal brain tumor classification](https://mlsanity.com/paper/2009.01592/) (2020)

## Built on by (most cited first, 3 of 3)

- [AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classification](https://mlsanity.com/paper/2406.15303/) (2024)
- [Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images](https://mlsanity.com/paper/2509.11034/) (2025)
- [A deep learning pipeline for PAM50 subtype classification using histopathology images and multi-objective patch selection](https://mlsanity.com/paper/2604.01798/) (2026)

## Abstract (arXiv)

In computational pathology, random sampling of patches during training of Multiple Instance Learning (MIL) methods is computationally efficient and serves as a regularization strategy. Despite its promising benefits, questions concerning performance trends for varying sample sizes and its influence on model interpretability remain. Addressing these, we reach an optimal performance enhancement of 1.7% using thirty percent of patches on the CAMELYON16 dataset, and 3.7% with only eight samples on the TUPAC16 dataset. We also find interpretability effects are strongly dataset-dependent, with interpretability impacted on CAMELYON16, while remaining unaffected on TUPAC16. This reinforces that both the performance and interpretability relationships with sampling are closely task-specific. End-to-end training with 1024 samples reveals improvements across both datasets compared to pre-extracted features, further highlighting the potential of this efficient approach.