Buchumschlag

Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Lai, Yuxiang, Zhong, Jike, Li, Ming, Zhao, Shitian, Li, Yuheng, Psounis, Konstantinos, Yang, Xiaofeng
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Computer Vision and Pattern Recognition
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Context Matters: Learning Global Semantics via Object-Centric Representation
von: Zhong, Jike, et al.
Veröffentlicht: (2025)

Are Video Models Emerging as Zero-Shot Learners and Reasoners in Medical Imaging?
von: Lai, Yuxiang, et al.
Veröffentlicht: (2025)

TIR-Bench: A Comprehensive Benchmark for Agentic Thinking-with-Images Reasoning
von: Li, Ming, et al.
Veröffentlicht: (2025)

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting
von: Li, Yuheng, et al.
Veröffentlicht: (2025)

EEE-Bench: A Comprehensive Multimodal Electrical And Electronics Engineering Benchmark
von: Li, Ming, et al.
Veröffentlicht: (2024)

VRIQ: Benchmarking and Analyzing Visual-Reasoning IQ of VLMs
von: Khezresmaeilzadeh, Tina, et al.
Veröffentlicht: (2026)

Think or Not Think: A Study of Explicit Thinking in Rule-Based Visual Reinforcement Fine-Tuning
von: Li, Ming, et al.
Veröffentlicht: (2025)

Patient-Specific Autoregressive Models for Organ Motion Prediction in Radiotherapy
von: Lai, Yuxiang, et al.
Veröffentlicht: (2025)

MedDINOv3: How to adapt vision foundation models for medical image segmentation?
von: Li, Yuheng, et al.
Veröffentlicht: (2025)

MedLVR: Latent Visual Reasoning for Reliable Medical Visual Question Answering
von: Xi, Suyang, et al.
Veröffentlicht: (2026)

MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning
von: Pan, Jiazhen, et al.
Veröffentlicht: (2025)

EchoVLM: Measurement-Grounded Multimodal Learning for Echocardiography
von: Li, Yuheng, et al.
Veröffentlicht: (2025)

MedSeg-R: Reasoning Segmentation in Medical Images with Multimodal Large Language Models
von: Huang, Yu, et al.
Veröffentlicht: (2025)

Med3D-R1: Incentivizing Clinical Reasoning in 3D Medical Vision-Language Models for Abnormality Diagnosis
von: Lai, Haoran, et al.
Veröffentlicht: (2026)

Affordance-R1: Reinforcement Learning for Generalizable Affordance Reasoning in Multimodal Large Language Model
von: Wang, Hanqing, et al.
Veröffentlicht: (2025)

MedReason-R1: Learning to Reason for CT Diagnosis with Reinforcement Learning and Local Zoom
von: Li, Yifan, et al.
Veröffentlicht: (2025)

ManipLVM-R1: Reinforcement Learning for Reasoning in Embodied Manipulation with Large Vision-Language Models
von: Song, Zirui, et al.
Veröffentlicht: (2025)

Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification
von: Li, Ming, et al.
Veröffentlicht: (2024)

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation
von: Yu, Wenjun, et al.
Veröffentlicht: (2025)

MedFact-R1: Towards Factual Medical Reasoning via Pseudo-Label Augmentation
von: Li, Gengliang, et al.
Veröffentlicht: (2025)

MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation
von: Koleilat, Taha, et al.
Veröffentlicht: (2026)

On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable?
von: Imam, Raza, et al.
Veröffentlicht: (2025)

PyVision-RL: Forging Open Agentic Vision Models via RL
von: Zhao, Shitian, et al.
Veröffentlicht: (2026)

TemMed-Bench: Evaluating Temporal Medical Image Reasoning in Vision-Language Models
von: Zhang, Junyi, et al.
Veröffentlicht: (2025)

Med-StepBench: A Hierarchical Reasoning Framework for Evaluating Hallucinations in Medical Vision-Language Models
von: Nguyen, Minh Khoi, et al.
Veröffentlicht: (2026)

Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning of Vision Language Models
von: Tan, Huajie, et al.
Veröffentlicht: (2025)

MedSeg-R: Medical Image Segmentation with Clinical Reasoning
von: Shao, Hao, et al.
Veröffentlicht: (2025)

VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
von: Yang, Senqiao, et al.
Veröffentlicht: (2025)

Vision-R1: Evolving Human-Free Alignment in Large Vision-Language Models via Vision-Guided Reinforcement Learning
von: Zhan, Yufei, et al.
Veröffentlicht: (2025)

Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding
von: Jiang, Songtao, et al.
Veröffentlicht: (2025)

VLA-R1: Enhancing Reasoning in Vision-Language-Action Models
von: Ye, Angen, et al.
Veröffentlicht: (2025)

PeRL: Permutation-Enhanced Reinforcement Learning for Interleaved Vision-Language Reasoning
von: Zhang, Yizhen, et al.
Veröffentlicht: (2025)

MultiMedVision: Multi-Modal Medical Vision Framework
von: Li, Frank, et al.
Veröffentlicht: (2026)

MedFoundationHub: A Lightweight and Secure Toolkit for Deploying Medical Vision Language Foundation Models
von: Li, Xiao, et al.
Veröffentlicht: (2025)

GMAI-VL-R1: Harnessing Reinforcement Learning for Multimodal Medical Reasoning
von: Su, Yanzhou, et al.
Veröffentlicht: (2025)

MedCLM: Learning to Localize and Reason via a CoT-Curriculum in Medical Vision-Language Models
von: Kim, Soo Yong, et al.
Veröffentlicht: (2025)

Towards Universal Text-driven CT Image Segmentation
von: Li, Yuheng, et al.
Veröffentlicht: (2025)

RL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation Models
von: Saremi, Parham, et al.
Veröffentlicht: (2025)

Boosting the Generalization and Reasoning of Vision Language Models with Curriculum Reinforcement Learning
von: Deng, Huilin, et al.
Veröffentlicht: (2025)

Med-Evo: Test-time Self-evolution for Medical Multimodal Large Language Models
von: Xu, Dunyuan, et al.
Veröffentlicht: (2026)