Work

Large-scale benchmarking and boosting transfer learning for medical image analysis

Year: 2025

Type: article

Source: Medical Image Analysis

DOI: 10.1016/j.media.2025.103487

Authors: Mohammad Reza Hosseinzadeh Taher, Fatemeh Haghighi, Michael B. Gotway, Jianming Liang

Institutions: Arizona State University, Mayo Clinic, Mayo Clinic in Arizona

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: closed

Funders: National Science Foundation, Mayo Clinic, Purdue University, Arizona State University, Eastman Chemical Company, National Institutes of Health, National Heart, Lung, and Blood Institute

Awards: Track 4: Advanced CI Coordination Ecosystem: Monitoring and Measurement Services, Track 1: ACCESS Resource Allocations Marketplace and Platform Services (RAMPS), ACO: An Open CI Ecosystem to Advance Scientific Discovery (OpenCI), G5057055846, G5066343944, Track 3: COre National Ecosystem for CyberinfrasTructure (CONECT), Computer-Aided Diagnosis of Pulmonary Embolism, G6472456535, Track 2: Customized Multi-tier Assistance, Training, and Computational Help (MATCH) for End User ACCESS to CI

OpenAlex ID: W4407835198

FWCI: 8.899

Cites: 135

Cited by: 22

Related to: 10