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Found 7 papers, 1 papers with code
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue
The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications.
Increasing the usefulness of already existing annotations through WSI registration
This indicates that WSI registration has the potential to reduce the need for IHC-specific annotations.
ACROBAT -- a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathology
In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections.
Using deep learning to detect patients at risk for prostate cancer despite benign biopsies
As a proof-of-principle, we developed and validated a deep convolutional neural network model to distinguish between morphological patterns in benign prostate biopsy whole slide images from men with and without established cancer.
Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks
Molecular phenotyping by gene expression profiling is common in contemporary cancer research and in molecular diagnostics.
Radiomic Feature Stability Analysis based on Probabilistic Segmentations
From these segmentations, we extract a high number of plausible feature vectors for each lung tumor and analyze feature variance with respect to the segmentations.
Image-based Survival Analysis for Lung Cancer Patients using CNNs
Traditional survival models such as the Cox proportional hazards model are typically based on scalar or categorical clinical features.
