# Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

arXiv: https://arxiv.org/abs/2304.05153 · HTML: https://mlsanity.com/paper/2304.05153/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 73 references that are in the map.
- First posted on arXiv 11 Apr 2023.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Balanced MSE for Imbalanced Visual Regression](https://mlsanity.com/paper/2203.16427/) (2022)
- [Fully Convolutional Multi-Class Multiple Instance Learning](https://mlsanity.com/paper/1412.7144/) (2014)
- [hist2RNA: An efficient deep learning architecture to predict gene expression from breast cancer histopathology images](https://mlsanity.com/paper/2304.04507/) (2023)
- [Attention-based Interpretable Regression of Gene Expression in Histology](https://mlsanity.com/paper/2208.13776/) (2022)
- [WeakSTIL: Weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need](https://mlsanity.com/paper/2109.05892/) (2021)
- [Contrastive learning-based computational histopathology predict differential expression of cancer driver genes](https://mlsanity.com/paper/2204.11994/) (2022)

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

- [HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis](https://mlsanity.com/paper/2406.16192/) (2024)
- [Towards Large-Scale Training of Pathology Foundation Models](https://mlsanity.com/paper/2404.15217/) (2024)
- [xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology](https://mlsanity.com/paper/2406.04280/) (2024)
- [Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning](https://mlsanity.com/paper/2411.13623/) (2024)
- [Robust Multimodal Survival Prediction with the Latent Differentiation Conditional Variational AutoEncoder](https://mlsanity.com/paper/2503.09496/) (2025)
- [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)
- [PH2ST:ST-Prompt Guided Histological Hypergraph Learning for Spatial Gene Expression Prediction](https://mlsanity.com/paper/2503.16816/) (2025)
- [Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology](https://mlsanity.com/paper/2403.03891/) (2024)
- [Histo-Miner: Deep learning based tissue features extraction pipeline from H&E whole slide images of cutaneous squamous cell carcinoma](https://mlsanity.com/paper/2505.04672/) (2025)
- [Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology](https://mlsanity.com/paper/2603.08328/) (2026)
- [Deep Multiple Instance Learning with Distance-Aware Self-Attention](https://mlsanity.com/paper/2305.10552/) (2023)
- [Cross-Cancer Knowledge Transfer in WSI-based Prognosis Prediction](https://mlsanity.com/paper/2508.13482/) (2025)
- [Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology](https://mlsanity.com/paper/2403.04558/) (2024)
- [Leveraging Support Vector Regression, Radiomics and Dosiomics for Outcome Prediction in Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy (PULSAR)](https://mlsanity.com/paper/2509.07872/) (2025)
- [Investigating the Impact of Histopathological Foundation Models on Regressive Prediction of Homologous Recombination Deficiency](https://mlsanity.com/paper/2602.00151/) (2026)

## Abstract (arXiv)

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesized that regression-based DL outperforms classification-based DL. Therefore, we developed and evaluated a new self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from images in 11,671 patients across nine cancer types. We tested our method for multiple clinically and biologically relevant biomarkers: homologous repair deficiency (HRD) score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the interpretability of the results over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.