ESICA delivers state-of-the-art accuracy on a five-modality 3D medical segmentation benchmark while offering a compact variant with far fewer parameters.
One model to rule them all: To- wards universal segmentation for medical images with text prompt
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DRD introduces a reprogramming module and CKA-based distillation to enable efficient, robust adaptation of medical foundation models to downstream 2D/3D classification and segmentation tasks, outperforming prior PEFT and KD methods on 18 tasks.
Anatomical location dominates prompt alignment in zero-shot VLM segmentation of NSCLC tumors, with VoxTell achieving DSC 0.613 comparable to fine-tuned baselines.
citing papers explorer
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ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation
ESICA delivers state-of-the-art accuracy on a five-modality 3D medical segmentation benchmark while offering a compact variant with far fewer parameters.
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Deep Reprogramming Distillation for Medical Foundation Models
DRD introduces a reprogramming module and CKA-based distillation to enable efficient, robust adaptation of medical foundation models to downstream 2D/3D classification and segmentation tasks, outperforming prior PEFT and KD methods on 18 tasks.
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Exploring Prompt Alignment with Clinical Factors in Zero-Shot Segmentation VLMs for NSCLC Tumor Segmentation
Anatomical location dominates prompt alignment in zero-shot VLM segmentation of NSCLC tumors, with VoxTell achieving DSC 0.613 comparable to fine-tuned baselines.