48 citations · 113 across the 7 of their papers we have counts for
8 papers
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…
Radiomic Feature Stability Analysis based on Probabilistic Segmentations
Christoph Haarburger, Justus Schock, Daniel Truhn +4
Identifying image features that are robust with respect to segmentation variability and domain shift is a tough challenge in radiomics. So far, this problem has mainly been tackled…
Image-based Survival Analysis for Lung Cancer Patients using CNNs
Christoph Haarburger, Philippe Weitz, Oliver Rippel +1
Traditional survival models such as the Cox proportional hazards model are typically based on scalar or categorical clinical features. With the advent of increasingly large image d…