Sophia J Wagner, Daniel Reisenbüchler, Nicholas P West, Jan Moritz Niehues, Jiefu Zhu, Sebastian Foersch, Gregory Patrick Veldhuizen, Philip Quirke, Heike I Grabsch, Piet A van den Brandt, Gordon G A Hutchins, Susan D Richman, Tanwei Yuan, Rupert Langer, Josien C A Jenniskens, Kelly Offermans, Wolfram Mueller, Richard Gray, Stephen B Gruber, Joel K Greenson, Gad Rennert, Joseph D Bonner, Daniel Schmolze, Jitendra Jonnagaddala, Nicholas J Hawkins, Robyn L Ward, Dion Morton, Matthew Seymour, Laura Magill, Marta Nowak, Jennifer Hay, Viktor H Koelzer, David N Church, Christian Matek, Carol Geppert, Chaolong Peng, Cheng Zhi, Xiaoming Ouyang, Jacqueline A James, Maurice B Loughrey, Manuel Salto-Tellez, Hermann Brenner, Michael Hoffmeister, Daniel Truhn, Julia A Schnabel, Melanie Boxberg, Tingying Peng, Jakob Nikolas Kather
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...
September 11, 2023: Cancer Cell