Zobrazeno 1 - 10
of 556
pro vyhledávání: '"Zhou, Hong-Yu"'
Autor:
Xie, Yunfei, Zhou, Ce, Gao, Lang, Wu, Juncheng, Li, Xianhang, Zhou, Hong-Yu, Liu, Sheng, Xing, Lei, Zou, James, Xie, Cihang, Zhou, Yuyin
This paper introduces MedTrinity-25M, a comprehensive, large-scale multimodal dataset for medicine, covering over 25 million images across 10 modalities, with multigranular annotations for more than 65 diseases. These enriched annotations encompass b
Externí odkaz:
http://arxiv.org/abs/2408.02900
Large Language Models (LLMs) show promise for natural language generation in healthcare, but risk hallucinating factually incorrect information. Deploying LLMs for medical question answering necessitates reliable uncertainty estimation (UE) methods t
Externí odkaz:
http://arxiv.org/abs/2407.08662
Recent advances in generative vision-language models (VLMs) have exciting potential implications for AI in radiology, yet VLMs are also known to produce hallucinations, nonsensical text, and other unwanted behaviors that can waste clinicians' time an
Externí odkaz:
http://arxiv.org/abs/2406.06496
Autor:
Gao, Fei, Wang, Siwen, Zhang, Fandong, Zhou, Hong-Yu, Wang, Yizhou, Wang, Churan, Yu, Gang, Yu, Yizhou
Medical image analysis suffers from a shortage of data, whether annotated or not. This becomes even more pronounced when it comes to 3D medical images. Self-Supervised Learning (SSL) can partially ease this situation by using unlabeled data. However,
Externí odkaz:
http://arxiv.org/abs/2406.00947
Current medical artificial intelligence systems are often limited to narrow applications, hindering their widespread adoption in clinical practice. To address this limitation, we propose MedVersa, a generalist learner that enables flexible learning a
Externí odkaz:
http://arxiv.org/abs/2405.07988
Automated classification of liver lesions in multi-phase CT and MR scans is of clinical significance but challenging. This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework, specifically designed for liver lesion class
Externí odkaz:
http://arxiv.org/abs/2402.17246
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
Parameter-efficient fine-tuning (PEFT) that was initially developed for exploiting pre-trained large language models has recently emerged as an effective approach to perform transfer learning on computer vision tasks. However, the effectiveness of PE
Externí odkaz:
http://arxiv.org/abs/2401.12215
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