December 8, 2025

TransMedVision: Improving Medical Image Analysis Under Data Scarcity With Transferable Visual Representations

Key Points

Accuracy improved to 0.9113 in few-shot COVID-19 pneumonia classification tasks, enhancing robustness.
Key metrics included F1 of 0.9032 and AUC of 0.9514, emphasising model precision in medical applications.
Assessment using transitional training strategies across diverse datasets showed adaptability of the model.
Highlights the potential for improved medical diagnosis through advanced data utilization and learning methods.

Abstract

ABSTRACT Accurate and robust medical image analysis plays a critical role in disease screening, diagnosis, and prognosis. However, its development is often constrained by data scarcity, privacy concerns, and domain discrepancies. To address these challenges, we propose TransMedVision—a transitional training framework tailored for cross‐domain few‐shot medical image analysis tasks. The framework consists of three stages: (1) initializing the model with a vision backbone pretrained on large‐scale natural image datasets; (2) performing short‐term transitional training on intermediate medical image datasets to reduce the representation gap between natural and medical domains, while stabilizing feature learning; and (3) fine‐tuning on the target few‐shot CT dataset to obtain the final classifier. By preserving general visual features and gradually adapting them to medical domains, TransMedVision enhances both cross‐domain transfer accuracy and training stability. In cross‐domain few‐shot COVID‐19 pneumonia CT classification tasks, TransMedVision achieves state‐of‐the‐art performance (Accuracy = 0.9113, F1 = 0.9032, AUC = 0.9514). All datasets, code, and models are publicly released via https://github.com/01Matrix/TransMedVision to facilitate reproducibility and future research.

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