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Prompting Vision Foundation Models for Pathology Image Analysis
Chong Yin
, Siqi Liu
,
Kaiyang Zhou
, Vincent Wai Sun Wong
,
Pong Chi Yuen
Department of Computer Science
Research output
:
Chapter in book/report/conference proceeding
›
Conference proceeding
›
peer-review
6
Citations (Scopus)
Overview
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Dive into the research topics of 'Prompting Vision Foundation Models for Pathology Image Analysis'. Together they form a unique fingerprint.
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Keyphrases
Foundation Models
100%
Prompting Method
100%
Pathology Image Analysis
100%
Non-alcoholic Fatty Liver Disease (NAFLD)
66%
Public Concern
33%
Function-based
33%
Instance-based
33%
Histogram Features
33%
Morphological Characteristics
33%
Diagnostic Performance
33%
Image Recognition
33%
Present Challenges
33%
Small Data
33%
Quantitative Attributes
33%
Attribute-based
33%
Spatial Attributes
33%
Spatial Histogram
33%
Visual Prompts
33%
K-functional
33%
Histopathological Images
33%
Tissue Alterations
33%
Small Dataset
33%
Liver Pathology
33%
Diverse Tasks
33%
Task-agnostic
33%
Computer Science
Image Analysis
100%
Foundation Model
100%
Paradigm Shift
33%
Quantitative Assessment
33%
Interpretability
33%
Quantitative Attribute
33%
Prompt Generator
33%
Engineering
Image Analysis
100%
Histogram
33%
Shortfall
33%
Image Recognition
33%
Public Concern
33%
K Function
33%
Interpretability
33%