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 …
In-context learning enables multimodal large language models to classify cancer pathology images
Medical image classification requires labeled, task-specific datasets which are used to train
deep learning networks de novo, or to fine-tune foundation models. However, this process is …
deep learning networks de novo, or to fine-tune foundation models. However, this process is …
RudolfV: a foundation model by pathologists for pathologists
Artificial intelligence has started to transform histopathology impacting clinical diagnostics
and biomedical research. However, while many computational pathology approaches have …
and biomedical research. However, while many computational pathology approaches have …
A foundational multimodal vision language AI assistant for human pathology
The field of computational pathology has witnessed remarkable progress in the
development of both task-specific predictive models and task-agnostic self-supervised vision …
development of both task-specific predictive models and task-agnostic self-supervised vision …
Label-efficient deep learning in medical image analysis: Challenges and future directions
Deep learning has seen rapid growth in recent years and achieved state-of-the-art
performance in a wide range of applications. However, training models typically requires …
performance in a wide range of applications. However, training models typically requires …
Domain-specific optimization and diverse evaluation of self-supervised models for histopathology
Task-specific deep learning models in histopathology offer promising opportunities for
improving diagnosis, clinical research, and precision medicine. However, development of …
improving diagnosis, clinical research, and precision medicine. However, development of …
Pathoduet: Foundation models for pathological slide analysis of H&E and IHC stains
Large amounts of digitized histopathological data display a promising future for developing
pathological foundation models via self-supervised learning methods. Foundation models …
pathological foundation models via self-supervised learning methods. Foundation models …
Practical Application of Deep Learning in Diagnostic Neuropathology—Reimagining a Histological Asset in the Era of Precision Medicine
Simple Summary Technological and scientific innovations, from genetic sequencing to
digital pathology slide scanners, have drastically altered the field of neuropathology. The …
digital pathology slide scanners, have drastically altered the field of neuropathology. The …
Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology
Developing computational pathology models is essential for reducing manual tissue typing
from whole slide images, transferring knowledge from the source domain to an unlabeled …
from whole slide images, transferring knowledge from the source domain to an unlabeled …
Pathotune: Adapting visual foundation model to pathological specialists
As natural image understanding moves towards the pretrain-finetune era, research in
pathology imaging is concurrently evolving. Despite the predominant focus on pretraining …
pathology imaging is concurrently evolving. Despite the predominant focus on pretraining …