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Found 7 papers, 0 papers with code
Masked Pre-Training of Transformers for Histology Image Analysis
However, due to the large number of model parameters and limited labeled data, applying transformer models to WSIs remains challenging.
MHAttnSurv: Multi-Head Attention for Survival Prediction Using Whole-Slide Pathology Images
In pathology, whole-slide images (WSI) based survival prediction has attracted increasing interest.
Resolution-Based Distillation for Efficient Histology Image Classification
Our results show that a combination of KD and self-supervision allows the student model to approach, and in some cases, surpass the classification accuracy of the teacher, while being much more efficient.
Predicting colorectal polyp recurrence using time-to-event analysis of medical records
Identifying patient characteristics that influence the rate of colorectal polyp recurrence can provide important insights into which patients are at higher risk for recurrence.
Deep neural networks for automated classification of colorectal polyps on histopathology slides: A multi-institutional evaluation
An accurate and automated algorithm for the classification of colorectal polyps on digitized histopathology slides could benefit clinicians and patients.
Automated detection of celiac disease on duodenal biopsy slides: a deep learning approach
In this study, we trained a deep learning model to detect celiac disease on duodenal biopsy images.
Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images
In this work, we built an automatic image-understanding method that can accurately classify different types of colorectal polyps in whole-slide histology images to help pathologists with histopathological characterization and diagnosis of colorectal polyps.
