Foundation Models and Information Retrieval in Digital Pathology
The paper reviews the state-of-the-art of foundation models, LLMs, generative AI, information retrieval and CBIR in digital pathology
Code (0)
등록된 구현이 없습니다.
Tasks
Information RetrievalRetrievalSimilar Papers 제목 키워드 기반
Self-Supervised Similarity Learning for Digital Pathology
Using features extracted from networks pretrained on ImageNet is a common practice in applications of deep learning for digital pathology. However it presents the downside of missing domain specific image information. In…
Image RetrievalRetrievalSelf-Supervised Learningwhole slide imagesStudying the Effect of Digital Stain Separation of Histopathology Images on Image Search Performance
Due to recent advances in technology, digitized histopathology images are now widely available for both clinical and research purposes. Accordingly, research into computerized image analysis algorithms for digital histop…
Image RetrievalRetrievalWhen is a Foundation Model a Foundation Model
Recently, several studies have reported on the fine-tuning of foundation models for image-text modeling in the field of medicine, utilizing images from online data sources such as Twitter and PubMed. Foundation models ar…
modelRetrievalComparing ImageNet Pre-training with Digital Pathology Foundation Models for Whole Slide Image-Based Survival Analysis
The abundance of information present in Whole Slide Images (WSIs) renders them an essential tool for survival analysis. Several Multiple Instance Learning frameworks proposed for this task utilize a ResNet50 backbone pre…
Multiple Instance LearningSurvival Analysiswhole slide imagesTowards 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 diseases and patient cohorts. However, mod…
Contrastive Learningimage-classificationImage ClassificationImage to text+3