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From the 1 of 24 linked papers with an AI index.

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24 papers

cs.CV2026

EvoGraph-R1: Self-Evolving Multimodal Knowledge Hypergraphs for Agentic Retrieval

Jiashi Lin, Changhong Jiang, Xiangru Lin +12

The paper proposes EvoGraph-R1, a framework that lets a retrieval agent dynamically evolve multimodal knowledge hypergraphs through actions like retrieval, web search, and graph ed…

cs.CV2026

UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis

Junzhi Ning, Wei Li, Cheng Tang +24

Medical workflows routinely combine reading images with producing visual and textual outputs, making both image understanding and generation central to medical AI. Most existing sy…

eess.IV2026

Unified Medical Image Tokenizer for Autoregressive Synthesis and Understanding

Chenglong Ma, Yuanfeng Ji, Jin Ye +9

Autoregressive modeling has driven major advances in multimodal AI, yet its application to medical imaging remains constrained by the absence of a unified image tokenizer that simu…

cs.CV2026

Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development

Zhongying Deng, Cheng Tang, Ziyan Huang +124

Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in…

cs.CV2026

MedQ-Engine: A Closed-Loop Data Engine for Evolving MLLMs in Medical Image Quality Assessment

Jiyao Liu, Junzhi Ning, Wanying Qu +4

Medical image quality assessment (Med-IQA) is a prerequisite for clinical AI deployment, yet multimodal large language models (MLLMs) still fall substantially short of human expert…

cs.CV2026

MedQ-UNI: Toward Unified Medical Image Quality Assessment and Restoration via Vision-Language Modeling

Jiyao Liu, Junzhi Ning, Wanying Qu +4

Existing medical image restoration (Med-IR) methods are typically modality-specific or degradation-specific, failing to generalize across the heterogeneous degradations encountered…