# Deep learning-based detection of morphological features associated with hypoxia in H&E breast cancer whole slide images

arXiv: https://arxiv.org/abs/2311.12601 · HTML: https://mlsanity.com/paper/2311.12601/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)

## Abstract (arXiv)

Hypoxia occurs when tumour cells outgrow their blood supply, leading to regions of low oxygen levels within the tumour. Calculating hypoxia levels can be an important step in understanding the biology of tumours, their clinical progression and response to treatment. This study demonstrates a novel application of deep learning to evaluate hypoxia in the context of breast cancer histomorphology. More precisely, we show that Weakly Supervised Deep Learning (WSDL) models can accurately detect hypoxia associated features in routine Hematoxylin and Eosin (H&E) whole slide images (WSI). We trained and evaluated a deep Multiple Instance Learning model on tiles from WSI H&E tissue from breast cancer primary sites (n=240) obtaining on average an AUC of 0.87 on a left-out test set. We also showed significant differences between features of hypoxic and normoxic tissue regions as distinguished by the WSDL models. Such DL hypoxia H&E WSI detection models could potentially be extended to other tumour types and easily integrated into the pathology workflow without requiring additional costly assays.