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20172026
most citedNon-rigid image registration using fully convolutional networks with deep self-supervision

76 citations · 178 across the 15 of their papers we have counts for

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17 papers · 1 filter

cs.CV2026

Learning Anatomy-Grounded CT Vision-Language Representations with Organ-Hierarchical Report Knowledge

Guoliang You, Hongming Li, Yuanwang Zhang +1

Medical vision-language pretraining (VLP) from paired CT images and radiology reports enables scalable representation learning, but most existing methods align either whole scans w…

cs.CV2023

Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation

Xiaoyang Chen, Hao Zheng, Yuemeng Li +4

A versatile medical image segmentation model applicable to images acquired with diverse equipment and protocols can facilitate model deployment and maintenance. However, building s…

cs.CV2020

Unsupervised deep learning for individualized brain functional network identification

Hongming Li, Yong Fan

A novel unsupervised deep learning method is developed to identify individual-specific large scale brain functional networks (FNs) from resting-state fMRI (rsfMRI) in an end-to-end…

cs.CV2020★ 3 cited

MDReg-Net: Multi-resolution diffeomorphic image registration using fully convolutional networks with deep self-supervision

Hongming Li, Yong Fan

We present a diffeomorphic image registration algorithm to learn spatial transformations between pairs of images to be registered using fully convolutional networks (FCNs) under a…

cs.CV2019★ 2 cited

Feature-Fused Context-Encoding Network for Neuroanatomy Segmentation

Yuemeng Li, Hangfan Liu, Hongming Li +1

Automatic segmentation of fine-grained brain structures remains a challenging task. Current segmentation methods mainly utilize 2D and 3D deep neural networks. The 2D networks take…

cs.CV2019★ 6 cited

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal MRI

Hongming Li, Mohamad Habes, David A. Wolk +1

Introduction: It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's dise…