2 citations · 3 across the 4 of their papers we have counts for
4 papers
A Mixture of Experts (MoE) model to improve AI-based computational pathology prediction performance under variable levels of histopathology image blur
Yujie Xiang, Bojing Liu, Mattias Rantalainen
AI-based models for histopathology whole slide image (WSI) analysis are increasingly common, but unsharp or blurred areas within WSI can significantly reduce prediction performance…
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…
Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unlabeled, unannotated pathology slides
Adalberto Claudio Quiros, Nicolas Coudray, Anna Yeaton +13
Definitive cancer diagnosis and management depend upon the extraction of information from microscopy images by pathologists. These images contain complex information requiring time…
Using deep learning to detect patients at risk for prostate cancer despite benign biopsies
Bojing Liu, Yinxi Wang, Philippe Weitz +6
Background: Transrectal ultrasound guided systematic biopsies of the prostate is a routine procedure to establish a prostate cancer diagnosis. However, the 10-12 prostate core biop…