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AgriCountDINO, a parameter-efficient exemplar-guided framework for joint counting and localization, is introduced, a parameter-efficient exemplar-guided framework for joint counting and localization that improves upon the best compared zero-shot method by 6.0\% without target-domain training or fine-tuning.
Manual delineation is time-consuming, and inter-reader variability is high, making accurate delineation of glioma subregions in multimodal magnetic resonance imaging (MRI) important for treatment planning and longitudinal assessment. Current automatic techniques have limitations in identifying small enhancing regions,...
A comprehensive analysis of the performance-forgetting trade-offs inherent in low-rank adaptation using principal components of weight matrices as initialization reveals that fine-tuning intermediate components leads to better balance and robustness to high learning rates than first (PiSSA) and last (MiLoRA) components...
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