Unleashing the potential of AI for pathology: challenges and recommendations
Computational pathology is currently witnessing a surge in the development of AI
techniques, offering promise for achieving breakthroughs and significantly impacting the …
techniques, offering promise for achieving breakthroughs and significantly impacting the …
[HTML][HTML] 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. To drive …
further understand the relationship between histology and patient outcome. To drive …
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. These vary from source to source and are the potential causes for domain-shift …
procedure. These vary from source to source and are the potential causes for domain-shift …
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 …
(CPath) by tackling intricate tasks across an array of histology image analysis applications …
Do Tissue Source Sites Leave Identifiable Signatures in Whole Slide Images Beyond Staining?
Why can deep learning predictors trained on Whole Slide Images fail to generalize? It is a
common theme in Computational Pathology to see a high performing model developed in a …
common theme in Computational Pathology to see a high performing model developed in a …
Harnessing artificial intelligence for transpathology advancements
Transpathology is an emerging theory that transcends the limitations of multiscale
measurements in the characterization of pathophysiology for disease phenotyping …
measurements in the characterization of pathophysiology for disease phenotyping …
Do Tissue Source Sites leave identifiable Signatures in Whole Slide Images beyond staining?
Why can deep learning predictors trained on Whole Slide Images fail to generalize? It is a
common theme in Computational Pathology to see a high performing model developed in a …
common theme in Computational Pathology to see a high performing model developed in a …
Transport-based morphometry of nuclear structures of digital pathology images in cancers
MSE Rabbi, N Ironside, JA Ozolek, R Singh… - ArXiv, 2023 - ncbi.nlm.nih.gov
Alterations in nuclear morphology are useful adjuncts and even diagnostic tools used by
pathologists in the diagnosis and grading of many tumors, particularly malignant tumors …
pathologists in the diagnosis and grading of many tumors, particularly malignant tumors …
[PDF][PDF] Unleashing the potential of AI for pathology
Computational pathology is currently witnessing a surge in the development of AI
techniques, offering promise for achieving breakthroughs and significantly impacting the …
techniques, offering promise for achieving breakthroughs and significantly impacting the …
[PDF][PDF] Investigating the effect of self-supervised contrastive learning on mitosis classification
TT Le Vuong, M Jahanifar, N Zamanitajeddin… - 27th Conference on … - research.ed.ac.uk
The quality of histology images depends on the tissue type, staining, and digitization
procedure, which vary from source to source, causing a domain-shift problem. Despite the …
procedure, which vary from source to source, causing a domain-shift problem. Despite the …