articleCancer CellAug 30, 2023HYBRID OA

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study

SJSophia J. WagnerDRDaniel ReisenbüchlerNPNicholas P. WestJNJan NiehuesJZJiefu Zhu

Helmholtz Zentrum München · Fresenius (Germany) · +38 more institutions

PubMed
Indexed incrossrefdatacitepubmed

Abstract

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over…

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Topics & keywords

Topics
Keywords
  • Interpretability
  • Generalizability theory
  • Computer science
  • Colorectal cancer
  • Medicine
  • Microsatellite instability
  • Deep learning
  • Artificial intelligence
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