activity
20232026
most citedLoCo: Locally Constrained Training-Free Layout-to-Image Synthesis

2 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

PathPilot: An Adaptive Question-Conditioned Multi-Magnification Navigation Agent for Whole-Slide Image Analysis

Zhengyang Xu, Han Li, Jingsong Liu +5

The diagnostic capability of WSI agents relies on effective slide navigation to identify diagnostically relevant regions and efficiently integrate evidence across the slide. While…

cs.CV2026

Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology

Han Li, Jingsong Liu, Ayako Ura +14

Uterine diseases represent an important category of gynecologic pathology and require accurate histopathological assessment for diagnosis and treatment planning. Whole-slide images…

cs.LG2026

Policy-Driven CT-Agent: Modeling Phase-Aware Diagnostic Control for Clinically Consistent CT Reasoning

Yanmeng Dong, Han Li, Yujia Li +7

Computed Tomography (CT) diagnosis often relies on dynamic selection of imaging phases, such as non-contrast, arterial, or venous phases, based on preliminary findings, clinical su…

cs.CV2026

Towards Voxel Spacing Consistency for Medical Image Segmentation

Xin You, Runze Yang, Minghui Zhang +6

Volumetric medical image segmentation is essential for both preoperative diagnosis and intraoperative guidance. While recent years have witnessed rapid progress in segmentation arc…

cs.CV2024

Skeleton Supervised Airway Segmentation

Mingyue Zhao, Han Li, Li Fan +3

Fully-supervised airway segmentation has accomplished significant triumphs over the years in aiding pre-operative diagnosis and intra-operative navigation. However, full voxel-leve…

cs.CV2023★ 2 cited

LoCo: Locally Constrained Training-Free Layout-to-Image Synthesis

Peiang Zhao, Han Li, Ruiyang Jin +1

Recent text-to-image diffusion models have reached an unprecedented level in generating high-quality images. However, their exclusive reliance on textual prompts often falls short…