Pan, S;
Secrier, M;
(2023)
HistoMIL: A Python package for training multiple instance learning models on histopathology slides.
iScience
, 26
(10)
, Article 108073. 10.1016/j.isci.2023.108073.
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Abstract
Hematoxylin and eosin (H&E) stained slides are widely used in disease diagnosis. Remarkable advances in deep learning have made it possible to detect complex molecular patterns in these histopathology slides, suggesting automated approaches could help inform pathologists’ decisions. Multiple instance learning (MIL) algorithms have shown promise in this context, outperforming transfer learning (TL) methods for various tasks, but their implementation and usage remains complex. We introduce HistoMIL, a Python package designed to streamline the implementation, training and inference process of MIL-based algorithms for computational pathologists and biomedical researchers. It integrates a self-supervised learning module for feature encoding, and a full pipeline encompassing TL and three MIL algorithms: ABMIL, DSMIL, and TransMIL. The PyTorch Lightning framework enables effortless customization and algorithm implementation. We illustrate HistoMIL's capabilities by building predictive models for 2,487 cancer hallmark genes on breast cancer histology slides, achieving AUROC performances of up to 85%.
Type: | Article |
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Title: | HistoMIL: A Python package for training multiple instance learning models on histopathology slides |
Location: | United States |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1016/j.isci.2023.108073 |
Publisher version: | https://doi.org/10.1016/j.isci.2023.108073 |
Language: | English |
Additional information: | © 2023 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Artificial intelligence, Histology, Machine learning |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment |
URI: | https://discovery.ucl.ac.uk/id/eprint/10179569 |
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