# From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology

arXiv: https://arxiv.org/abs/2312.10944 · HTML: https://mlsanity.com/paper/2312.10944/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 79 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary](https://mlsanity.com/paper/2006.13932/) (2020)
- [MHAttnSurv: Multi-Head Attention for Survival Prediction Using Whole-Slide Pathology Images](https://mlsanity.com/paper/2110.11558/) (2021)
- [Regression-based Deep-Learning predicts molecular biomarkers from pathology slides](https://mlsanity.com/paper/2304.05153/) (2023)
- [FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology](https://mlsanity.com/paper/2011.06033/) (2020)
- [Attention-based Interpretable Regression of Gene Expression in Histology](https://mlsanity.com/paper/2208.13776/) (2022)

## Built on by (most cited first, 4 of 4)

- [In-context learning enables multimodal large language models to classify cancer pathology images](https://mlsanity.com/paper/2403.07407/) (2024)
- [Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology](https://mlsanity.com/paper/2403.03891/) (2024)
- [INSIGHT: Explainable Weakly-Supervised Medical Image Analysis](https://mlsanity.com/paper/2412.02012/) (2024)
- [Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology](https://mlsanity.com/paper/2403.04558/) (2024)

## Abstract (arXiv)

Hematoxylin- and eosin (H&E) stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology enabled the prediction of biomarkers directly from WSIs. However, accurately linking tissue phenotype to biomarkers at scale remains a crucial challenge for democratizing complex biomarkers in precision oncology. This protocol describes a practical workflow for solid tumor associative modeling in pathology (STAMP), enabling prediction of biomarkers directly from WSIs using deep learning. The STAMP workflow is biomarker agnostic and allows for genetic- and clinicopathologic tabular data to be included as an additional input, together with histopathology images. The protocol consists of five main stages which have been successfully applied to various research problems: formal problem definition, data preprocessing, modeling, evaluation and clinical translation. The STAMP workflow differentiates itself through its focus on serving as a collaborative framework that can be used by clinicians and engineers alike for setting up research projects in the field of computational pathology. As an example task, we applied STAMP to the prediction of microsatellite instability (MSI) status in colorectal cancer, showing accurate performance for the identification of MSI-high tumors. Moreover, we provide an open-source codebase which has been deployed at several hospitals across the globe to set up computational pathology workflows. The STAMP workflow requires one workday of hands-on computational execution and basic command line knowledge.