Search Results for author:
Found 10 papers, 3 papers with code
Deep Blur Multi-Model (DeepBlurMM) -- a strategy to mitigate the impact of image blur on deep learning model performance in histopathology image analysis
We propose a multi-model approach, i. e. DeepBlurMM, to alleviate the impact of unsharp image areas and improve the model performance.
WEEP: A method for spatial interpretation of weakly supervised CNN models in computational pathology
Deep learning enables the modelling of high-resolution histopathology whole-slide images (WSI).
Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis
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 performance and posing a challenge for widespread clinical application as fine-tuning algorithms for each new site is impractical.
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.
Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression-morphology analysis in breast cancer
The relationship between morphology and molecular phenotype has a potential to be exploited for prediction of the molecular phenotype from the morphology visible in histopathology images.
Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence
We additionally evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology.
