Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology

NG Laleh, HS Muti, CML Loeffler, A Echle… - Medical image …, 2022 - Elsevier
Artificial intelligence (AI) can extract visual information from histopathological slides and
yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of …

[HTML][HTML] Artificial intelligence for detection of microsatellite instability in colorectal cancer—a multicentric analysis of a pre-screening tool for clinical application

A Echle, NG Laleh, P Quirke, HI Grabsch, HS Muti… - ESMO open, 2022 - Elsevier
Background Microsatellite instability (MSI)/mismatch repair deficiency (dMMR) is a key
genetic feature which should be tested in every patient with colorectal cancer (CRC) …

[HTML][HTML] Artificial intelligence–based detection of FGFR3 mutational status directly from routine histology in bladder cancer: a possible preselection for molecular …

CML Loeffler, NO Bruechle, M Jung, L Seillier… - European urology …, 2022 - Elsevier
Background Fibroblast growth factor receptor (FGFR) inhibitor treatment has become the first
clinically approved targeted therapy in bladder cancer. However, it requires previous …

Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning

OL Saldanha, HS Muti, HI Grabsch, R Langer, B Dislich… - Gastric cancer, 2023 - Springer
Background Computational pathology uses deep learning (DL) to extract biomarkers from
routine pathology slides. Large multicentric datasets improve performance, but such …

Benchmarking artificial intelligence methods for end-to-end computational pathology

NG Laleh, HS Muti, CM Lavinia Loeffler, A Echle… - Biorxiv, 2021 - biorxiv.org
Artificial intelligence (AI) can extract subtle visual information from digitized histopathology
slides and yield scientific insight on genotype-phenotype interactions as well as clinically …