Search Results for author:
Found 5 papers, 5 papers with code
Towards a text-based quantitative and explainable histopathology image analysis
Herein, we hypothesize that the pre-trained vision-language models can be utilized for quantitative histopathology image analysis through a simple image-to-text retrieval.
FALFormer: Feature-aware Landmarks self-attention for Whole-slide Image Classification
Herein, we propose an efficient and effective slide-level classification model, named as FALFormer, that can process a WSI as a whole so as to fully exploit the relationship among the entire patches and to improve the classification performance.
MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image Analysis
Specifically, we employ a student-teacher framework to learn a target model from a pre-trained, teacher model without direct access to source data and distill relevant knowledge via momentum contrastive learning with multi-head attention mechanism, which provides consistent and context-aware feature representations.
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification
The appearance of histopathology images depends on tissue type, staining and digitization procedure.
