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Found 9 papers, 5 papers with code
Domain Generalization in Computational Pathology: Survey and Guidelines
Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) by tackling intricate tasks across an array of histology image analysis applications.
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.
Centroid-aware feature recalibration for cancer grading in pathology images
Cancer grading is an essential task in pathology.
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.
GradMix for nuclei segmentation and classification in imbalanced pathology image datasets
The experimental results suggest that GradMix is able to improve the performance of nuclei segmentation and classification in imbalanced pathology image datasets.
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.
HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow.
Ranked #2 on
Multi-tissue Nucleus Segmentation
on CoNSeP
Methods for Segmentation and Classification of Digital Microscopy Tissue Images
Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis.
BACH: Grand Challenge on Breast Cancer Histology Images
From the submitted algorithms it was possible to push forward the state-of-the-art in terms of accuracy (87%) in automatic classification of breast cancer with histopathological images.
