# Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling

arXiv: https://arxiv.org/abs/2206.08885 · HTML: https://mlsanity.com/paper/2206.08885/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [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)
- [Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks](https://mlsanity.com/paper/2107.13048/) (2021)
- [Interpretable Survival Prediction for Colorectal Cancer using Deep Learning](https://mlsanity.com/paper/2011.08965/) (2020)
- [DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer](https://mlsanity.com/paper/2107.09405/) (2021)
- [Learning to rank for censored survival data](https://mlsanity.com/paper/1806.01984/) (2018)
- [Joint and individual analysis of breast cancer histologic images and genomic covariates](https://mlsanity.com/paper/1912.00434/) (2019)

## Built on by (most cited first, 11 of 11)

- [Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology](https://mlsanity.com/paper/2405.11643/) (2024)
- [A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models](https://mlsanity.com/paper/2407.06508/) (2024)
- [Multimodal Prototyping for cancer survival prediction](https://mlsanity.com/paper/2407.00224/) (2024)
- [HVTSurv: Hierarchical Vision Transformer for Patient-Level Survival Prediction from Whole Slide Image](https://mlsanity.com/paper/2306.17373/) (2023)
- [Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspective](https://mlsanity.com/paper/2407.07841/) (2024)
- [Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2408.09476/) (2024)
- [Multi-Scale Relational Graph Convolutional Network for Multiple Instance Learning in Histopathology Images](https://mlsanity.com/paper/2212.08781/) (2022)
- [A Short Survey on Set-Based Aggregation Techniques for Single-Vector WSI Representation in Digital Pathology](https://mlsanity.com/paper/2409.04615/) (2024)
- [DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology](https://mlsanity.com/paper/2508.15208/) (2025)
- [Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments](https://mlsanity.com/paper/2406.07061/) (2024)
- [Graph Mamba Survival Analysis Based on Topology-Aware ordering](https://mlsanity.com/paper/2606.02602/) (2026)

## Abstract (arXiv)

Supervised learning tasks such as cancer survival prediction from gigapixel whole slide images (WSIs) are a critical challenge in computational pathology that requires modeling complex features of the tumor microenvironment. These learning tasks are often solved with deep multi-instance learning (MIL) models that do not explicitly capture intratumoral heterogeneity. We develop a novel variance pooling architecture that enables a MIL model to incorporate intratumoral heterogeneity into its predictions. Two interpretability tools based on representative patches are illustrated to probe the biological signals captured by these models. An empirical study with 4,479 gigapixel WSIs from the Cancer Genome Atlas shows that adding variance pooling onto MIL frameworks improves survival prediction performance for five cancer types.