57 citations · 143 across the 16 of their papers we have counts for
8 papers · 1 filter
Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small Vessel Enhancement and Morphological Correction
Dongning Song, Weijian Huang, Jiarun Liu +3
Accurate segmentation of blood vessels is essential for various clinical assessments and postoperative analyses. However, the inherent challenges of vascular imaging, such as spars…
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…
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…
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…
A multi-modal vision-language model for generalizable annotation-free pathology localization
Hao Yang, Hong-Yu Zhou, Jiarun Liu +12
Existing deep learning models for defining pathology from clinical imaging data rely on expert annotations and lack generalization capabilities in open clinical environments. Here,…
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…