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A deep learning framework for efficient pathology image analysis

Abstract Artificial intelligence has transformed digital pathology by enabling biomarker prediction from high-resolution whole-slide images. However, current methods are computationally inefficient, processing thousands of redundant tiles per slide and requiring complex aggregation models. We introd...

Deskribapen osoa

Gorde:
Egile Nagusiak: Peter Neidlinger, Tim Lenz, Sebastian Foersch, Chiara M. L. Loeffler, Jan Clusmann, Marco Gustav, Lawrence A. Shaktah, Rupert Langer, Bastian Dislich, Lisa A. Boardman, Amy J. French, Ellen L. Goode, Andrea Gsur, Stefanie Brezina, Marc J. Gunter, Robert Steinfelder, Hans-Michael Behrens, Christoph Röcken, Tabitha Harrison, Ulrike Peters, Amanda I. Phipps, Giuseppe Curigliano, Nicola Fusco, Antonio Marra, Michael Hoffmeister, Hermann Brenner, Jakob Nikolas Kather
Formatua: Artigo
Hizkuntza:Inglês
Argitaratua: Nature Portfolio 2026-07-01
Saila:Nature Communications
Sarrera elektronikoa:https://doi.org/10.1038/s41467-026-74918-9
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