# Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images

arXiv: https://arxiv.org/abs/1702.05803 · HTML: https://mlsanity.com/paper/1702.05803/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2017.

## Built on by (most cited first, 8 of 8)

- [Machine Learning Methods for Histopathological Image Analysis: A Review](https://mlsanity.com/paper/2102.03889/) (2021)
- [Interpretable Prediction of Lung Squamous Cell Carcinoma Recurrence With Self-supervised Learning](https://mlsanity.com/paper/2203.12204/) (2022)
- [Hard Negative Sample Mining for Whole Slide Image Classification](https://mlsanity.com/paper/2410.02212/) (2024)
- [Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning](https://mlsanity.com/paper/2108.02278/) (2021)
- [ModalTune: Fine-Tuning Slide-Level Foundation Models with Multi-Modal Information for Multi-task Learning in Digital Pathology](https://mlsanity.com/paper/2503.17564/) (2025)
- [A comprehensive review of deep learning in lung cancer](https://mlsanity.com/paper/2308.02528/) (2023)
- [Improved Breast Cancer Diagnosis through Transfer Learning on Hematoxylin and Eosin Stained Histology Images](https://mlsanity.com/paper/2309.08745/) (2023)
- [Breast Tumor Classification Using EfficientNet Deep Learning Model](https://mlsanity.com/paper/2411.17870/) (2024)

## Abstract (arXiv)

Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture. Consequently, most of the automated systems have focused on characterizing the epithelial regions of the breast to detect cancer. In this paper, we propose a system for classification of hematoxylin and eosin (H&E) stained breast specimens based on convolutional neural networks that primarily targets the assessment of tumor-associated stroma to diagnose breast cancer patients. We evaluate the performance of our proposed system using a large cohort containing 646 breast tissue biopsies. Our evaluations show that the proposed system achieves an area under ROC of 0.92, demonstrating the discriminative power of previously neglected tumor-associated stroma as a diagnostic biomarker.