3 papers
eess.IV2025
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…
eess.IV2024
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…
q-bio.GN2024
Evaluation and Prognostic Validation of Deep Regression Models for WSI-Based Gene-Expression Prediction
Fredrik K. Gustafsson, Mattias Rantalainen, Constance Boissin +1
Gene-expression profiling is widely used in research and central to many areas of precision oncology, but remains costly and not universally accessible. Recent advances in computat…