Work

Autodidactic dense anatomical models

Year: 2026

Type: article

Source: Medical Image Analysis

DOI: 10.1016/j.media.2026.104117

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

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

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Purdue University, Arizona State University, 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), 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: W7160280873

FWCI:

Cites: 56

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