activity
20192026
most citedTopology-Preserving Deep Image Segmentation

85 citations · 136 across the 33 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.CV2025

LoC-Path: Learning to Compress for Pathology Multimodal Large Language Models

Qingqiao Hu, Weimin Lyu, Meilong Xu +5

Whole Slide Image (WSI) MLLMs are difficult to build and deploy because gigapixel slides induce thousands of visual tokens, while only a small fraction of regions is diagnostically…

cs.CV2025

FLAT: FLow-Aligned Training of Unrolled Networks for MRI Reconstruction

Kehan Qi, Saumya Gupta, Xiaoling Hu +4

Unrolled networks are widely used in Magnetic Resonance Imaging (MRI) reconstruction for their efficiency. Structured as a series of neural network stages (or cascades), an unrolle…

cs.CV2025

RB-FT: Rationale-Bootstrapped Fine-Tuning for Video Classification

Meilong Xu, Di Fu, Jiaxing Zhang +7

Vision Language Models (VLMs) are becoming increasingly integral to multimedia understanding; however, they often struggle with domain-specific video classification tasks, particul…

cs.CV2025

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation

Meilong Xu, Xiaoling Hu, Shahira Abousamra +2

In semi-supervised segmentation, capturing meaningful semantic structures from unlabeled data is essential. This is particularly challenging in histopathology image analysis, where…

cs.CV2025

Uncertainty Estimation for Pretrained Medical Image Registration Models via Transformation Equivariance

Lin Tian, Xiaoling Hu, Juan Eugenio Iglesias

Accurate image registration is essential in many medical imaging applications, yet most deep registration networks provide little indication of when or where their predictions are…

cs.CV2025

Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA

Chen Li, Meilong Xu, Xiaoling Hu +2

Robust medical image segmentation across modalities remains challenging due to severe domain shifts and the lack of target-domain labels. While diffusion models have been explored…