23 citations · 28 across the 3 of their papers we have counts for
3 papers
WEEP: A method for spatial interpretation of weakly supervised CNN models in computational pathology
Abhinav Sharma, Bojing Liu, Mattias Rantalainen
Deep learning enables the modelling of high-resolution histopathology whole-slide images (WSI). Weakly supervised learning of tile-level data is typically applied for tasks where l…
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue
Philippe Weitz, Masi Valkonen, Leslie Solorzano +38
The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability…
ACROBAT -- a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathology
Philippe Weitz, Masi Valkonen, Leslie Solorzano +15
The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is an essential part of the pathologic assessment of surgically resecte…