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

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-bas...

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Detalles Bibliográficos
Autores principales: Wagner, Sophia J., Reisenbüchler, Daniel, West, Nicholas P., Niehues, Jan Moritz, Zhu, Jiefu, Foersch, Sebastian, Veldhuizen, Gregory Patrick, Quirke, Philip, Grabsch, Heike I., van den Brandt, Piet A., Hutchins, Gordon G.A., Richman, Susan D., Yuan, Tanwei, Langer, Rupert, Jenniskens, Josien C.A., Offermans, Kelly, Mueller, Wolfram, Gray, Richard, Gruber, Stephen B., Greenson, Joel K., Rennert, Gad, Bonner, Joseph D., Schmolze, Daniel, Jonnagaddala, Jitendra, Hawkins, Nicholas J., Ward, Robyn L., Morton, Dion, Seymour, Matthew, Magill, Laura, Nowak, Marta, Hay, Jennifer, Koelzer, Viktor H., Church, David N., Matek, Christian, Geppert, Carol, Peng, Chaolong, Zhi, Cheng, Ouyang, Xiaoming, James, Jacqueline A., Loughrey, Maurice B., Salto-Tellez, Manuel, Brenner, Hermann, Hoffmeister, Michael, Truhn, Daniel, Schnabel, Julia A., Boxberg, Melanie, Peng, Tingying, Kather, Jakob Nikolas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cell Press 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10507381/
https://www.ncbi.nlm.nih.gov/pubmed/37652006
http://dx.doi.org/10.1016/j.ccell.2023.08.002

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