# Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images

arXiv: https://arxiv.org/abs/2409.09053 · HTML: https://mlsanity.com/paper/2409.09053/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 42 references that are in the map.
- First posted on arXiv 30 Aug 2024.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review](https://mlsanity.com/paper/2306.01546/) (2023)
- [Breast Cancer Molecular Subtypes Prediction on Pathological Images with Discriminative Patch Selecting and Multi-Instance Learning](https://mlsanity.com/paper/2203.07659/) (2022)

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

- [Computational Methods for Breast Cancer Molecular Profiling through Routine Histopathology: A Review](https://mlsanity.com/paper/2412.10392/) (2024)
- [Automating tumor-infiltrating lymphocyte assessment in breast cancer histopathology images using QuPath: a transparent and accessible machine learning pipeline](https://mlsanity.com/paper/2504.16979/) (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)

Classifying breast cancer molecular subtypes is crucial for tailoring treatment strategies. While immunohistochemistry (IHC) and gene expression profiling are standard methods for molecular subtyping, IHC can be subjective, and gene profiling is costly and not widely accessible in many regions. Previous approaches have highlighted the potential application of deep learning models on H&E-stained whole slide images (WSI) for molecular subtyping, but these efforts vary in their methods, datasets, and reported performance. In this work, we investigated whether H&E-stained WSIs could be solely leveraged to predict breast cancer molecular subtypes (luminal A, B, HER2-enriched, and Basal). We used 1,433 WSIs of breast cancer in a two-step pipeline: first, classifying tumor and non-tumor tiles to use only the tumor regions for molecular subtyping; and second, employing a One-vs-Rest (OvR) strategy to train four binary OvR classifiers and aggregating their results using an eXtreme Gradient Boosting (XGBoost) model. The pipeline was tested on 221 hold-out WSIs, achieving an overall macro F1 score of 0.95 for tumor detection and 0.73 for molecular subtyping. Our findings suggest that, with further validation, supervised deep learning models could serve as supportive tools for molecular subtyping in breast cancer. Our codes are made available to facilitate ongoing research and development.