Back to search

Article

Text-Guided Synthesis in Medical Multimedia Retrieval: A Framework for Enhanced Colonoscopy Image Classification and Segmentation

2025-01-03

Abstract excerpt

The lack of extensive, varied, and thoroughly annotated datasets impedes the advancement of Artificial Intelligence (AI) for medical applications, specially colorectal cancer detection. Models trained with limited diversity often display biases, especially when utilized on disadvantaged groups. Generative models (e.g., DALL-E 2, VQ-GAN) ‘have been used to generate images, but not colonoscopy data for intelligent d...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
1e49e01e-2c34-516a-9cfb-d766a0d1ee0c
DOI
10.20944/preprints202501.0236.v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Text-Guided Synthesis in Medical Multimedia Retrieval: A Framework for Enhanced Colonoscopy Image Classification and SegmentationDOI 10.20944/preprints202501.0236.v1
Select a neighboring publication to make it the new centre.