CERS integrates LLM-generated CoT reasoning, a knowledge pool, semantic reference selection, and a multi-scale attention module to improve semi-supervised medical image segmentation beyond visual pattern matching.
arXiv preprint arXiv:2005.06465 (2020)
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A unified autoregressive vision-language framework integrates segmentation, detection, and appearance reasoning for CT images via task-routing tokens and progressive refinement, with gains on public benchmarks.
A teacher–student semi-supervised framework with alignment-preserving patch mixing, position-aware text augmentation, and positional contrastive learning improves medical referring segmentation at low label ratios.
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.
Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.
citing papers explorer
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Beyond Visual Cues: CoT-Enhanced Reasoning for Semi-supervised Medical Image Segmentation
CERS integrates LLM-generated CoT reasoning, a knowledge pool, semantic reference selection, and a multi-scale attention module to improve semi-supervised medical image segmentation beyond visual pattern matching.
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Segmentation, Detection and Explanation: A Unified Framework for CT Appearance Reasoning
A unified autoregressive vision-language framework integrates segmentation, detection, and appearance reasoning for CT images via task-routing tokens and progressive refinement, with gains on public benchmarks.
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Semi-MedRef: Semi-Supervised Medical Referring Image Segmentation with Cross-Modal Alignment
A teacher–student semi-supervised framework with alignment-preserving patch mixing, position-aware text augmentation, and positional contrastive learning improves medical referring segmentation at low label ratios.
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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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APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms
Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.