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20212026
most citedAsymmetric 3D Context Fusion for Universal Lesion Detection

25 citations · 41 across the 25 of their papers we have counts for

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21 papers · 1 filter

cs.CV2026

DDX-TRACE: A Benchmark for Medical Diagnostic Trajectories in VLMs

Jiazhen Pan, Weixiang Shen, Jun Li +7

Medical diagnosis is not a single prediction from a fully specified vignette. It is a sequential workup: clinicians decide what evidence to obtain, revise a differential diagnosis,…

cs.CV2026

GenMed: A Pairwise Generative Reformulation of Medical Diagnostic Tasks

Hantao Zhang, Weidong Guo, Yuhe Liu +5

Data-driven medical AI is traditionally formulated as a discriminative mapping from input to output via a learned function , which does not generalize well across hetero…

cs.CV2026

PlaneCycle: Training-Free 2D-to-3D Lifting of Foundation Models Without Adapters

Yinghong Yu, Guangyuan Li, Jiancheng Yang

Large-scale 2D foundation models exhibit strong transferable representations, yet extending them to 3D volumetric data typically requires retraining, adapters, or architectural red…

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

Refining 3D Medical Segmentation with Verbal Instruction

Kangxian Xie, Jiancheng Yang, Nandor Pinter +3

Accurate 3D anatomical segmentation is essential for clinical diagnosis and surgical planning. However, automated models frequently generate suboptimal shape predictions due to fac…

cs.CV2026

EyeWorld: A Generative World Model of Ocular State and Dynamics

Ziyu Gao, Xinyuan Wu, Xiaolan Chen +10

Ophthalmic decision-making depends on subtle lesion-scale cues interpreted across multimodal imaging and over time, yet most medical foundation models remain static and degrade und…