Zobrazeno 1 - 10
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pro vyhledávání: '"Liu, Jiarun"'
Autor:
Liu, Jiarun, Yang, Hao, Zhou, Hong-Yu, Xi, Yan, Yu, Lequan, Yu, Yizhou, Liang, Yong, Shi, Guangming, Zhang, Shaoting, Zheng, Hairong, Wang, Shanshan
Accurate medical image segmentation demands the integration of multi-scale information, spanning from local features to global dependencies. However, it is challenging for existing methods to model long-range global information, where convolutional n
Externí odkaz:
http://arxiv.org/abs/2402.03302
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, 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
Large Language Models (LLMs) have emerged as promising agents for web navigation tasks, interpreting objectives and interacting with web pages. However, the efficiency of spliced prompts for such tasks remains underexplored. We introduces AllTogether
Externí odkaz:
http://arxiv.org/abs/2310.18331
Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face a number of challenges, such as the requirement for fine-grained knowledge u
Externí odkaz:
http://arxiv.org/abs/2309.05904
In the field of few-shot learning (FSL), extensive research has focused on improving network structures and training strategies. However, the role of data processing modules has not been fully explored. Therefore, in this paper, we propose Meta-DM, a
Externí odkaz:
http://arxiv.org/abs/2305.08092
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