The Performance of Transferability Metrics Does Not Translate to Medical Tasks

Author(s)

Chaves, Levy ; Bissoto, Alceu ; Valle, Eduardo ; Avila, Sandra

• Document type: Journal article
• Source: DOMAIN ADAPTATION AND REPRESENTATION TRANSFER, DART 2023; v. 14293, p. 10-pg., 2024-01-01.

Abstract

Transfer learning boosts the performance of medical image analysis by enabling deep learning (DL) on small datasets through the knowledge acquired from large ones. As the number of DL architectures explodes, exhaustively attempting all candidates becomes unfeasible, motivating cheaper alternatives for choosing them. Transferability scoring methods emerge as an enticing solution, allowing to efficiently calculate a score that correlates with the architecture accuracy on any target dataset. However, since transferability scores have not been evaluated on medical datasets, their use in this context remains uncertain, preventing them from benefiting practitioners. We fill that gap in this work, thoroughly evaluating seven transferability scores in three medical applications, including out-of-distribution scenarios. Despite promising results in general-purpose datasets, our results show that no transferability score can reliably and consistently estimate target performance in medical contexts, inviting further work in that direction. (AU)

• FAPESP's process: 13/08293-7 - CCES - Center for Computational Engineering and Sciences
• Grantee: Munir Salomao Skaf
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 20/09838-0 - BI0S - Brazilian Institute of Data Science
• Grantee: João Marcos Travassos Romano
• Support Opportunities: Research Grants - Research Centers in Engineering Program
• FAPESP's process: 22/09606-8 - Understanding the role of shortcuts and distribution shifts in deep learning generalization
• Grantee: Alceu Emanuel Bissoto
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• FAPESP's process: 19/19619-7 - Generating unlimited skin lesion images with generative adversarial networks
• Grantee: Alceu Emanuel Bissoto
• Support Opportunities: Scholarships in Brazil - Doctorate