Transferable Low-Rank Convolutional Bases for Onboarding Unseen Medical Imaging Modalities
Deploying a medical imaging model that must later accommodate a modality it has never seen is a recurring practical problem: retraining the shared representation is expensive and destroys performance on the modalities already in service. We study this \emph{onboarding} problem under a strict leave-one-domain-out protocol, in which a convolutional backbone is pre-trained on source modalities (Kidney CT and Brain MRI), frozen permanently, and then required to accommodate an unseen modality (Chest X-ray). Under this protocol we establish three findings. First, decision-layer parameter-efficient fine-tuning is insufficient when the backbone has never observed the target modality: a linear pro...
| Authors | Ranat Das Prangon, Istiaque Ahmed, Shajid Hasan Naim et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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