Synergizing Discriminative Exemplars and Self-Refined Experience for MLLM-based In-Context Learning in Medical Diagnosis

arXiv:2603.27737 · cs.CV · Submitted 2026-03-29 · Read on arXiv

cs.CV

Submitted: 2026-03-29

Updated: 2026-09-26

Terminology

• In-Context Learning — 14× in this paper · explained in 4 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Self-Refined Experience Summarization — 6× in this paper
• Discriminative Exemplar Coreset Selection — 4× in this paper
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• In-Context Learning (ICL) — 3× in this paper · explained in 5 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Optical Coherence Tomography — 1× in this paper
• Semantic Gap — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Spectral clustering — 1× in this paper · explained in 2 episodes

Sources

• Training-Free Group Relative Policy Optimization
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• K-Prism: A Knowledge-Guided and Prompt Integrated Universal Medical Image Segmentation Model
• Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding
• MedGemma Technical Report
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

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