On the challenges and perspectives of foundation models for medical image analysis
This article discusses the opportunities, applications and future directions of large-scale
pretrained models, ie, foundation models, which promise to significantly improve the …
pretrained models, ie, foundation models, which promise to significantly improve the …
Application of artificial intelligence in pathology: trends and challenges
I Kim, K Kang, Y Song, TJ Kim - Diagnostics, 2022 - mdpi.com
Given the recent success of artificial intelligence (AI) in computer vision applications, many
pathologists anticipate that AI will be able to assist them in a variety of digital pathology …
pathologists anticipate that AI will be able to assist them in a variety of digital pathology …
A visual–language foundation model for pathology image analysis using medical twitter
The lack of annotated publicly available medical images is a major barrier for computational
research and education innovations. At the same time, many de-identified images and much …
research and education innovations. At the same time, many de-identified images and much …
A visual-language foundation model for computational pathology
The accelerated adoption of digital pathology and advances in deep learning have enabled
the development of robust models for various pathology tasks across a diverse array of …
the development of robust models for various pathology tasks across a diverse array of …
Towards a visual-language foundation model for computational pathology
The accelerated adoption of digital pathology and advances in deep learning have enabled
the development of powerful models for various pathology tasks across a diverse array of …
the development of powerful models for various pathology tasks across a diverse array of …
[HTML][HTML] One model is all you need: multi-task learning enables simultaneous histology image segmentation and classification
The recent surge in performance for image analysis of digitised pathology slides can largely
be attributed to the advances in deep learning. Deep models can be used to initially localise …
be attributed to the advances in deep learning. Deep models can be used to initially localise …
Omnimedvqa: A new large-scale comprehensive evaluation benchmark for medical lvlm
Abstract Large Vision-Language Models (LVLMs) have demonstrated remarkable
capabilities in various multimodal tasks. However their potential in the medical domain …
capabilities in various multimodal tasks. However their potential in the medical domain …
OCELOT: overlapped cell on tissue dataset for histopathology
Cell detection is a fundamental task in computational pathology that can be used for
extracting high-level medical information from whole-slide images. For accurate cell …
extracting high-level medical information from whole-slide images. For accurate cell …
A real-world dataset and benchmark for foundation model adaptation in medical image classification
Foundation models, often pre-trained with large-scale data, have achieved paramount
success in jump-starting various vision and language applications. Recent advances further …
success in jump-starting various vision and language applications. Recent advances further …
NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images
A Mahbod, C Polak, K Feldmann, R Khan, K Gelles… - Scientific Data, 2024 - nature.com
In computational pathology, automatic nuclei instance segmentation plays an essential role
in whole slide image analysis. While many computerized approaches have been proposed …
in whole slide image analysis. While many computerized approaches have been proposed …