# MHAttnSurv: Multi-Head Attention for Survival Prediction Using Whole-Slide Pathology Images

arXiv: https://arxiv.org/abs/2110.11558 · HTML: https://mlsanity.com/paper/2110.11558/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 63 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (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)
- [Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks](https://mlsanity.com/paper/2009.11169/) (2020)
- [Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://mlsanity.com/paper/1811.12808/) (2018)
- [Deep learning-based survival prediction for multiple cancer types using histopathology images](https://mlsanity.com/paper/1912.07354/) (2019)
- [Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks](https://mlsanity.com/paper/1901.11489/) (2019)
- [Interpretable Survival Prediction for Colorectal Cancer using Deep Learning](https://mlsanity.com/paper/2011.08965/) (2020)
- [Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides](https://mlsanity.com/paper/1811.08513/) (2018)
- [Scheduled DropHead: A Regularization Method for Transformer Models](https://mlsanity.com/paper/2004.13342/) (2020)

## Built on by (most cited first, 15 of 15)

- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)
- [Masked Pre-Training of Transformers for Histology Image Analysis](https://mlsanity.com/paper/2304.07434/) (2023)
- [Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction](https://mlsanity.com/paper/2403.10581/) (2024)
- [Multi-Attention Multiple Instance Learning](https://mlsanity.com/paper/2112.06071/) (2021)
- [From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology](https://mlsanity.com/paper/2312.10944/) (2023)
- [PS3: A Multimodal Transformer Integrating Pathology Reports with Histology Images and Biological Pathways for Cancer Survival Prediction](https://mlsanity.com/paper/2509.20022/) (2025)
- [Survival Analysis as Imprecise Classification with Trainable Kernels](https://mlsanity.com/paper/2506.10140/) (2025)
- [HiLa: Hierarchical Vision-Language Collaboration for Cancer Survival Prediction](https://mlsanity.com/paper/2507.04613/) (2025)
- [Predicting Targeted Therapy Resistance in Non-Small Cell Lung Cancer Using Multimodal Machine Learning](https://mlsanity.com/paper/2503.24165/) (2025)
- [Whole Slide Concepts: A Supervised Foundation Model For Pathological Images](https://mlsanity.com/paper/2507.05742/) (2025)
- [Multiple Instance Learning with Trainable Decision Tree Ensembles](https://mlsanity.com/paper/2302.06601/) (2023)
- [Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records](https://mlsanity.com/paper/2410.09880/) (2024)
- [INSIGHT: Spatially resolved survival modelling from routine histology crosslinked with molecular profiling reveals prognostic epithelial-immune axes in stage II/III colorectal cancer](https://mlsanity.com/paper/2512.22262/) (2025)
- [Breaking the Correlation Plateau: On the Optimization and Capacity Limits of Attention-Based Regressors](https://mlsanity.com/paper/2602.17898/) (2026)
- [HGP-Mamba: Integrating Histology and Generated Protein Features for Mamba-based Multimodal Survival Risk Prediction](https://mlsanity.com/paper/2603.16421/) (2026)

## Abstract (arXiv)

In pathology, whole-slide images (WSI) based survival prediction has attracted increasing interest. However, given the large size of WSIs and the lack of pathologist annotations, extracting the prognostic information from WSIs remains a challenging task. Previous studies have used multiple instance learning approaches to combine the information from multiple randomly sampled patches, but different visual patterns may contribute differently to prognosis prediction. In this study, we developed a multi-head attention approach to focus on various parts of a tumor slide, for more comprehensive information extraction from WSIs. We evaluated our approach on four cancer types from The Cancer Genome Atlas database. Our model achieved an average c-index of 0.640, outperforming two existing state-of-the-art approaches for WSI-based survival prediction, which have an average c-index of 0.603 and 0.619 on these datasets. Visualization of our attention maps reveals each attention head focuses synergistically on different morphological patterns.