ASAP: Automated Style-Aware Similarity Measurement for Selection of Annotated Pre-Training Datasets in 2D Biomedical Imaging

ASAP: Automated Style-Aware Similarity Measurement for Selection of Annotated Pre-Training Datasets in 2D Biomedical Imaging

Medical imaging scenarios are characterized by varying image modalities, several organs/cell shapes, and little annotated data because of the expertise required for labeling. The successful use of state-of-the-art deep-learning approaches requires a large amount of annotated data or a pre-trained mo...

Full description

Saved in:
Bibliographic Details
Main Authors: Miguel Molina-Moreno, Marcel P. Schilling, Markus Reischl, Ralf Mikut
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Biomedical imaging
meta-learning
similarity
style transfer
transfer learning
Online Access:https://ieeexplore.ieee.org/document/10942339/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/10942339/

Similar Items