GI‐ScreenNet v2: A Modular Framework for Gastrointestinal Disease Detection Based on an Integrated Transfer Learning
We present a unified framework for GI disease detection, with an integrated workflow for dynamic model selection and cross-attention fusion. This design enables efficient integration of novel models and techniques, advancing robust diagnostic systems.
Authors
- Changshun Yang (ORCID: https://orcid.org/0000-0003-4001-2148)
- Leyi Wei (ORCID: https://orcid.org/0000-0003-1444-190X)
- Chen Su (ORCID: https://orcid.org/0000-0003-3571-6554)
- Shan Lin (ORCID: https://orcid.org/0000-0003-4868-0650)
- Qiaocao Su
- Xiaorong Liu
Institutions
- Fujian Medical University (CN)
- Fujian Provincial Hospital (CN)
- Macao Polytechnic University (MO)
- Xiamen University of Technology (CN)
- Fuzhou University (CN)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- International Journal of Medical Robotics and Computer Assisted Surgery
- Published
- 2025-12-25
- DOI
- https://doi.org/10.1002/rcs.70128
- Primary Topic
- Gastrointestinal Bleeding Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00