Search Results for author:
Found 5 papers, 2 papers with code
Nucleus subtype classification using inter-modality learning
In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E.
Cell Spatial Analysis in Crohn's Disease: Unveiling Local Cell Arrangement Pattern with Graph-based Signatures
This analysis is based on data collected at the two research institutes.
Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images
Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training.
Cross-scale Multi-instance Learning for Pathological Image Diagnosis
Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology.
Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images
Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotations.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
