Zobrazeno 41 - 50
of 2 293
pro vyhledávání: '"Li, Xiaomeng"'
Autor:
Zeng, An, Wu, Chunbiao, Huang, Meiping, Zhuang, Jian, Bi, Shanshan, Pan, Dan, Ullah, Najeeb, Khan, Kaleem Nawaz, Wang, Tianchen, Shi, Yiyu, Li, Xiaomeng, Lin, Guisen, Xu, Xiaowei
Publikováno v:
Computerized Medical Imaging and Graphics, 2023
Cardiovascular disease (CVD) accounts for about half of non-communicable diseases. Vessel stenosis in the coronary artery is considered to be the major risk of CVD. Computed tomography angiography (CTA) is one of the widely used noninvasive imaging m
Externí odkaz:
http://arxiv.org/abs/2211.01607
Left-ventricular ejection fraction (LVEF) is an important indicator of heart failure. Existing methods for LVEF estimation from video require large amounts of annotated data to achieve high performance, e.g. using 10,030 labeled echocardiogram videos
Externí odkaz:
http://arxiv.org/abs/2210.11291
Learning how to generalize the model to unseen domains is an important area of research. In this paper, we propose TripleE, and the main idea is to encourage the network to focus on training on subsets (learning with replay) and enlarge the data spac
Externí odkaz:
http://arxiv.org/abs/2210.01807
Publikováno v:
STACOM2022.13593(2022)383-391
Developing a deep learning method for medical segmentation tasks heavily relies on a large amount of labeled data. However, the annotations require professional knowledge and are limited in number. Recently, semi-supervised learning has demonstrated
Externí odkaz:
http://arxiv.org/abs/2209.15451
Fully supervised semantic segmentation learns from dense masks, which requires heavy annotation cost for closed set. In this paper, we use natural language as supervision without any pixel-level annotation for open world segmentation. We call the pro
Externí odkaz:
http://arxiv.org/abs/2209.13558
Contrastive Language-Image Pre-training (CLIP) learns rich representations via readily available supervision of natural language. It improves the performance of downstream vision tasks, including but not limited to the zero-shot, long tail, segmentat
Externí odkaz:
http://arxiv.org/abs/2209.07046
Video shadow detection aims to generate consistent shadow predictions among video frames. However, the current approaches suffer from inconsistent shadow predictions across frames, especially when the illumination and background textures change in a
Externí odkaz:
http://arxiv.org/abs/2208.00150
This paper addresses the problem of few-shot skin disease classification by introducing a novel approach called the Sub-Cluster-Aware Network (SCAN) that enhances accuracy in diagnosing rare skin diseases. The key insight motivating the design of SCA
Externí odkaz:
http://arxiv.org/abs/2207.01072
Developing an AI-assisted gland segmentation method from histology images is critical for automatic cancer diagnosis and prognosis; however, the high cost of pixel-level annotations hinders its applications to broader diseases. Existing weakly-superv
Externí odkaz:
http://arxiv.org/abs/2206.06665
Autor:
ZHANG Zixuan, ZHOU Jin, SUN Anran, TANG Yaoyu, ZHAO Lianzhen, GUO Xueping, ZHANG Lishi, LI Xiaomeng, WANG Yingyao
Publikováno v:
Zhongguo shipin weisheng zazhi, Vol 35, Iss 12, Pp 1764-1770 (2023)
Hyaluronic acid (HA), a natural substance widely present in the human body, exhibits diverse physiological regulatory effects and is often found in the structurally stable form known as sodium hyaluronate (SH). HA finds extensive use in t
Externí odkaz:
https://doaj.org/article/504bf339216c4888877b068726968b25