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pro vyhledávání: '"HUANG, Weijian"'
Accurate segmentation of blood vessels is essential for various clinical assessments and postoperative analyses. However, the inherent challenges of vascular imaging, such as sparsity, fine granularity, low contrast, data distribution variability, an
Externí odkaz:
http://arxiv.org/abs/2411.15251
Autor:
Huang, Weijian, Li, Cheng, Yang, Hao, Liu, Jiarun, Liang, Yong, Zheng, Hairong, Wang, Shanshan
Recently, vision-language representation learning has made remarkable advancements in building up medical foundation models, holding immense potential for transforming the landscape of clinical research and medical care. The underlying hypothesis is
Externí odkaz:
http://arxiv.org/abs/2401.11421
Autor:
Liu, Jiarun, Zhou, Hong-Yu, Li, Cheng, Huang, Weijian, Yang, Hao, Liang, Yong, Wang, Shanshan
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 datasets is usually orders of magnitude smaller than that in natural datas
Externí odkaz:
http://arxiv.org/abs/2401.01591
Autor:
Huang, Weijian, Li, Cheng, Zhou, Hong-Yu, Liu, Jiarun, Yang, Hao, Liang, Yong, Shi, Guangming, Zheng, Hairong, Wang, Shanshan
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 clinical applications. While previous studies have commonly focused on
Externí odkaz:
http://arxiv.org/abs/2401.01583
Autor:
Yang, Hao, Zhou, Hong-Yu, Li, Zhihuan, Gao, Yuanxu, Li, Cheng, Huang, Weijian, Liu, Jiarun, Zheng, Hairong, Zhang, Kang, Wang, Shanshan
Defining pathologies automatically from medical images aids the understanding of the emergence and progression of diseases, and such an ability is crucial in clinical diagnostics. However, existing deep learning models heavily rely on expert annotati
Externí odkaz:
http://arxiv.org/abs/2401.02044
Autor:
Yang, Hao, Zhou, Hong-Yu, Li, Cheng, Huang, Weijian, Liu, Jiarun, Liang, Yong, Shi, Guangming, Zheng, Hairong, Liu, Qiegen, Wang, Shanshan
Multimodal deep learning utilizing imaging and diagnostic reports has made impressive progress in the field of medical imaging diagnostics, demonstrating a particularly strong capability for auxiliary diagnosis in cases where sufficient annotation in
Externí odkaz:
http://arxiv.org/abs/2401.01524
Background: Deep learning has presented great potential in accurate MR image segmentation when enough labeled data are provided for network optimization. However, manually annotating 3D MR images is tedious and time-consuming, requiring experts with
Externí odkaz:
http://arxiv.org/abs/2312.10978
Autor:
Huang, Weijian, Li, Cheng, Zhou, Hong-Yu, Yang, Hao, Liu, Jiarun, Liang, Yong, Zheng, Hairong, Zhang, Shaoting, Wang, Shanshan
Publikováno v:
Nature Communications 15, 7620 (2024)
Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face crucial challenges, such as the requirement for fine-grained knowledge under
Externí odkaz:
http://arxiv.org/abs/2309.05904
The ability to incrementally learn new classes from limited samples is crucial to the development of artificial intelligence systems for real clinical application. Although existing incremental learning techniques have attempted to address this issue
Externí odkaz:
http://arxiv.org/abs/2304.05734
Multi-modal representation methods have achieved advanced performance in medical applications by extracting more robust features from multi-domain data. However, existing methods usually need to train additional branches for downstream tasks, which m
Externí odkaz:
http://arxiv.org/abs/2303.08562