57 citations · 84 across the 18 of their papers we have counts for
18 papers
CNsEMD: An Expert-Annotated Multi-Field-Strength MRI Dataset and a Hyperspherical Manifold Network for Multimodal Cranial Nerve Parcellation
Lei Xie, Junxiong Huang, Guoqiang Xie +9
Cranial nerves (CNs) play essential roles in sensory, motor, and autonomic functions. Accurate CN parcellation from multimodal magnetic resonance imaging (MRI) is crucial for neuro…
Cross-Sequence Semi-Supervised Learning for Multi-Parametric MRI-Based Visual Pathway Delineation
Alou Diakite, Cheng Li, Lei Xie +5
Accurately delineating the visual pathway (VP) is crucial for understanding the human visual system and diagnosing related disorders. Exploring multi-parametric MR imaging data has…
Spatial-Angular Representation Learning for High-Fidelity Continuous Super-Resolution in Diffusion MRI
Ruoyou Wu, Jian Cheng, Cheng Li +5
Diffusion magnetic resonance imaging (dMRI) often suffers from low spatial and angular resolution due to inherent limitations in imaging hardware and system noise, adversely affect…
AID-DTI: Accelerating High-fidelity Diffusion Tensor Imaging with Detail-preserving Model-based Deep Learning
Wenxin Fan, Jian Cheng, Cheng Li +4
Deep learning has shown great potential in accelerating diffusion tensor imaging (DTI). Nevertheless, existing methods tend to suffer from Rician noise and eddy current, leading to…
DeepMpMRI: Tensor-decomposition Regularized Learning for Fast and High-Fidelity Multi-Parametric Microstructural MR Imaging
Wenxin Fan, Jian Cheng, Qiyuan Tian +4
Deep learning has emerged as a promising approach for learning the nonlinear mapping between diffusion-weighted MR images and tissue parameters, which enables automatic and deep un…
Automating Vessel Segmentation in the Heart and Brain: A Trend to Develop Multi-Modality and Label-Efficient Deep Learning Techniques
Nazik Elsayed, Yousuf Babiker M. Osman, Cheng Li +2
Cardio-cerebrovascular diseases are the leading causes of mortality worldwide, whose accurate blood vessel segmentation is significant for both scientific research and clinical usa…
CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learning
Ruoyou Wu, Jian Cheng, Cheng Li +5
Deep learning-based dMRI super-resolution methods can effectively enhance image resolution by leveraging the learning capabilities of neural networks on large datasets. However, th…
Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining
Jiarun Liu, Hao Yang, Hong-Yu Zhou +8
Accurate medical image segmentation demands the integration of multi-scale information, spanning from local features to global dependencies. However, it is challenging for existing…
Enhancing Representation in Medical Vision-Language Foundation Models via Multi-Scale Information Extraction Techniques
Weijian Huang, Cheng Li, Hong-Yu Zhou +6
The development of medical vision-language foundation models has attracted significant attention in the field of medicine and healthcare due to their promising prospect in various…
AID-DTI: Accelerating High-fidelity Diffusion Tensor Imaging with Detail-Preserving Model-based Deep Learning
Wenxin Fan, Jian Cheng, Cheng Li +6
Deep learning has shown great potential in accelerating diffusion tensor imaging (DTI). Nevertheless, existing methods tend to suffer from Rician noise and detail loss in reconstru…
Modality Exchange Network for Retinogeniculate Visual Pathway Segmentation
Hua Han, Cheng Li, Lei Xie +3
Accurate segmentation of the retinogeniculate visual pathway (RGVP) aids in the diagnosis and treatment of visual disorders by identifying disruptions or abnormalities within the p…
Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging
Jing Yang, Jian Cheng, Cheng Li +4
Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the \textit{in…
LESEN: Label-Efficient deep learning for Multi-parametric MRI-based Visual Pathway Segmentation
Alou Diakite, Cheng Li, Lei Xie +3
Recent research has shown the potential of deep learning in multi-parametric MRI-based visual pathway (VP) segmentation. However, obtaining labeled data for training is laborious a…
MLIP: Medical Language-Image Pre-training with Masked Local Representation Learning
Jiarun Liu, Hong-Yu Zhou, Cheng Li +4
Existing contrastive language-image pre-training aims to learn a joint representation by matching abundant image-text pairs. However, the number of image-text pairs in medical data…
Enhancing the vision-language foundation model with key semantic knowledge-emphasized report refinement
Weijian Huang, Cheng Li, Hao Yang +4
Recently, vision-language representation learning has made remarkable advancements in building up medical foundation models, holding immense potential for transforming the landscap…
Multimodal self-supervised learning for lesion localization
Hao Yang, Hong-Yu Zhou, Cheng Li +7
Multimodal deep learning utilizing imaging and diagnostic reports has made impressive progress in the field of medical imaging diagnostics, demonstrating a particularly strong capa…
Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation
Yousuf Babiker M. Osman, Cheng Li, Weijian Huang +1
Background: Deep learning has presented great potential in accurate MR image segmentation when enough labeled data are provided for network optimization. However, manually annotati…
Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning
Weijian Huang, Cheng Li, Hong-Yu Zhou +6
Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face crucia…