arXiv Machine Learning
UniPrompt-CL: Efficient Single-Inference Continual Learning for Medical Image Classification
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arXiv Machine Learning
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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification
arXiv:2607. 29462v1 Announce Type: cross Abstract: Adapting deep learning models to profound clinical heterogeneity typically relies on parameter-efficient fine-tuning (PEFT) to avoid the severe overfitting associated with full end-to-end network updates.
arXiv Machine Learning
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CAMEL: Cross-Architecture Masked Embedding Learning for Ultra-Lightweight Retinal Foundation Models
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arXiv Computer Vision
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Less Is More in the Long Tail: Stage-Adaptive Sample Selection for Annotation-Efficient Dense Prediction
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arXiv Machine Learning
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Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration
arXiv:2407. 13632v2 Announce Type: replace-cross Abstract: Deploying deep learning-based imaging tools across various clinical sites poses significant challenges due to inherent domain shifts and regulatory hurdles associated with site-specific fine-tuning.
arXiv AI
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GRIN+: Towards Fast Yet Effective Machine Unlearning for Imbalanced Medical Data
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arXiv AI
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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning
arXiv:2607. 29353v1 Announce Type: cross Abstract: With the ever-increasing pervasiveness of smart edge devices, the demand is growing for applications that can be tailored to users (e.