23 citations · 27 across the 4 of their papers we have counts for
4 papers
Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction
Fredrik K. Gustafsson, Constance Boissin, Johan Vallon-Christersson +2
Pathology foundation models (PFMs) have recently emerged as powerful pretrained encoders for computational pathology, enabling transfer learning across a wide range of downstream t…
Evaluation and Prognostic Validation of Deep Regression Models for WSI-Based Gene-Expression Prediction
Fredrik K. Gustafsson, Constance Boissin, Johan Vallon-Christersson +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…
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…