bioRxivCancer BiologyPreprint
Self-supervised deep learning for pan-cancer mutation prediction from histopathology
O. L. Saldanha, C. M. L. Loeffler, J. M. Niehues, M. van Treeck, T. P. Seraphin, K. J. Hewitt, D. Cifci, G. P. Veldhuizen, S. Ramesh, A. T. Pearson, J. N. Kather
Abstract
The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep learning can predict genetic alterations from tissue morphology, but it is unclear how well these predictions generalize to external datasets. Here, we present a deep learning pipeline based on self-supervised feature extraction which achieves a robust predictability of genetic alterations in two large multicentric datasets of seven tumor types.
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Pan-cancer image-based detection of clinically actionable genetic alterations
Jakob Nikolas Kather, Lara Rosaline Heij, … Tom Luedde·Nature CancerPeer-reviewed·Jul 27, 2020
- A Deep Learning solution for triaging patient with cancer from their predicted mutational status using histopathological images
Louis-Oscar Morel, Valentin Dérangère, … Nathan Vinçon·bioRxiv·Jan 30, 2022
- Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images using Deep Learning
N. Coudray, A. L. Moreira, … A. Tsirigos·bioRxiv·Oct 3, 2017
- Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis
Yu Fu, Alexander W. Jung, … Moritz Gerstung·Nature CancerPeer-reviewed·Jul 27, 2020
- Deep learning for cancer type classification
- Predicting cancer outcomes from histology and genomics using convolutional networks
Pooya Mobadersany, Safoora Yousefi, … Lee Cooper·Proceedings of the National Academy of SciencesPeer-reviewed·Mar 12, 2018