31 citations · 95 across the 11 of their papers we have counts for
11 papers
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…
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…
Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis
Xiaoyi Ji, Richard Salmon, Nita Mulliqi +14
The potential of artificial intelligence (AI) in digital pathology is limited by technical inconsistencies in the production of whole slide images (WSIs), leading to degraded AI pe…
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…
Increasing the usefulness of already existing annotations through WSI registration
Philippe Weitz, Viktoria Sartor, Balazs Acs +4
Computational pathology methods have the potential to improve access to precision medicine, as well as the reproducibility and accuracy of pathological diagnoses. Particularly the…
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…
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…
Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks
Philippe Weitz, Yinxi Wang, Kimmo Kartasalo +5
Molecular phenotyping by gene expression profiling is common in contemporary cancer research and in molecular diagnostics. However, molecular profiling remains costly and resource…
Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression-morphology analysis in breast cancer
Yinxi Wang, Kimmo Kartasalo, Masi Valkonen +4
Molecular phenotyping is central in cancer precision medicine, but remains costly and standard methods only provide a tumour average profile. Microscopic morphological patterns obs…
Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence
Peter Ström, Kimmo Kartasalo, Henrik Olsson +29
Background: An increasing volume of prostate biopsies and a world-wide shortage of uro-pathologists puts a strain on pathology departments. Additionally, the high intra- and inter-…