Zobrazeno 1 - 10
of 5 501
pro vyhledávání: '"Zhang, Lihua"'
Most medical image lesion segmentation methods rely on hand-crafted accurate annotations of the original image for supervised learning. Recently, a series of weakly supervised or unsupervised methods have been proposed to reduce the dependence on pix
Externí odkaz:
http://arxiv.org/abs/2406.14958
Autor:
Jiang, Yue, Chen, Jiawei, Yang, Dingkang, Li, Mingcheng, Wang, Shunli, Wu, Tong, Li, Ke, Zhang, Lihua
When Large Vision Language Models (LVLMs) are applied to multimodal medical generative tasks, they suffer from significant model hallucination issues. This severely impairs the model's generative accuracy, making it challenging for LVLMs to be implem
Externí odkaz:
http://arxiv.org/abs/2406.11451
Autor:
Chen, Jiawei, Yang, Dingkang, Wu, Tong, Jiang, Yue, Hou, Xiaolu, Li, Mingcheng, Wang, Shunli, Xiao, Dongling, Li, Ke, Zhang, Lihua
Large Vision Language Models (LVLMs) are increasingly integral to healthcare applications, including medical visual question answering and imaging report generation. While these models inherit the robust capabilities of foundational Large Language Mo
Externí odkaz:
http://arxiv.org/abs/2406.10185
Autor:
Yang, Dingkang, Wei, Jinjie, Xiao, Dongling, Wang, Shunli, Wu, Tong, Li, Gang, Li, Mingcheng, Wang, Shuaibing, Chen, Jiawei, Jiang, Yue, Xu, Qingyao, Li, Ke, Zhai, Peng, Zhang, Lihua
Developing intelligent pediatric consultation systems offers promising prospects for improving diagnostic efficiency, especially in China, where healthcare resources are scarce. Despite recent advances in Large Language Models (LLMs) for Chinese medi
Externí odkaz:
http://arxiv.org/abs/2405.19266
Autor:
Qian, Ziyun, Xiao, Zeyu, Wu, Zhenyi, Yang, Dingkang, Li, Mingcheng, Wang, Shunli, Wang, Shuaibing, Kou, Dongliang, Zhang, Lihua
Motion style transfer is a significant research direction in multimedia applications. It enables the rapid switching of different styles of the same motion for virtual digital humans, thus vastly increasing the diversity and realism of movements. It
Externí odkaz:
http://arxiv.org/abs/2405.02844
Autor:
Han, Minghao, Zhang, Xukun, Yang, Dingkang, Liu, Tao, Kuang, Haopeng, Feng, Jinghui, Zhang, Lihua
Survival prediction is a complex ordinal regression task that aims to predict the survival coefficient ranking among a cohort of patients, typically achieved by analyzing patients' whole slide images. Existing deep learning approaches mainly adopt mu
Externí odkaz:
http://arxiv.org/abs/2404.19334
Autor:
Li, Mingcheng, Yang, Dingkang, Zhao, Xiao, Wang, Shuaibing, Wang, Yan, Yang, Kun, Sun, Mingyang, Kou, Dongliang, Qian, Ziyun, Zhang, Lihua
Multimodal sentiment analysis (MSA) aims to understand human sentiment through multimodal data. Most MSA efforts are based on the assumption of modality completeness. However, in real-world applications, some practical factors cause uncertain modalit
Externí odkaz:
http://arxiv.org/abs/2404.16456
Autor:
Chen, Jiawei, Yang, Dingkang, Jiang, Yue, Li, Mingcheng, Wei, Jinjie, Hou, Xiaolu, Zhang, Lihua
In the realm of Medical Visual Language Models (Med-VLMs), the quest for universal efficient fine-tuning mechanisms remains paramount, especially given researchers in interdisciplinary fields are often extremely short of training resources, yet large
Externí odkaz:
http://arxiv.org/abs/2404.16385
Autor:
Wang, Yuzheng, Yang, Dingkang, Chen, Zhaoyu, Liu, Yang, Liu, Siao, Zhang, Wenqiang, Zhang, Lihua, Qi, Lizhe
Data-Free Knowledge Distillation (DFKD) is a promising task to train high-performance small models to enhance actual deployment without relying on the original training data. Existing methods commonly avoid relying on private data by utilizing synthe
Externí odkaz:
http://arxiv.org/abs/2403.19539
Autor:
Chen, Jiawei, Jiang, Yue, Yang, Dingkang, Li, Mingcheng, Wei, Jinjie, Qian, Ziyun, Zhang, Lihua
While large language models (LLMs) excel in world knowledge understanding, adapting them to specific subfields requires precise adjustments. Due to the model's vast scale, traditional global fine-tuning methods for large models can be computationally
Externí odkaz:
http://arxiv.org/abs/2403.06407