Zobrazeno 1 - 10
of 1 087
pro vyhledávání: '"Walsh, Simon"'
Autor:
Wang, Shiyi, Nan, Yang, Zhang, Sheng, Felder, Federico, Xing, Xiaodan, Fang, Yingying, Del Ser, Javier, Walsh, Simon L F, Yang, Guang
In pulmonary tracheal segmentation, the scarcity of annotated data is a prevalent issue in medical segmentation. Additionally, Deep Learning (DL) methods face challenges: the opacity of 'black box' models and the need for performance enhancement. Our
Externí odkaz:
http://arxiv.org/abs/2407.03542
The manifestation of symptoms associated with lung diseases can vary in different depths for individual patients, highlighting the significance of 3D information in CT scans for medical image classification. While Vision Transformer has shown superio
Externí odkaz:
http://arxiv.org/abs/2406.17173
In the field of medical imaging, particularly in tasks related to early disease detection and prognosis, understanding the reasoning behind AI model predictions is imperative for assessing their reliability. Conventional explanation methods encounter
Externí odkaz:
http://arxiv.org/abs/2406.15182
In medical imaging, particularly in early disease detection and prognosis tasks, discerning the rationale behind an AI model's predictions is crucial for evaluating the reliability of its decisions. Conventional explanation methods face challenges in
Externí odkaz:
http://arxiv.org/abs/2406.18552
Each medical segmentation task should be considered with a specific AI algorithm based on its scenario so that the most accurate prediction model can be obtained. The most popular algorithms in medical segmentation, 3D U-Net and its variants, can dir
Externí odkaz:
http://arxiv.org/abs/2402.07403
Autor:
Nan, Yang, Xing, Xiaodan, Wang, Shiyi, Tang, Zeyu, Felder, Federico N, Zhang, Sheng, Ledda, Roberta Eufrasia, Ding, Xiaoliu, Yu, Ruiqi, Liu, Weiping, Shi, Feng, Sun, Tianyang, Cao, Zehong, Zhang, Minghui, Gu, Yun, Zhang, Hanxiao, Gao, Jian, Wang, Pingyu, Tang, Wen, Yu, Pengxin, Kang, Han, Chen, Junqiang, Lu, Xing, Zhang, Boyu, Mamalakis, Michail, Prinzi, Francesco, Carlini, Gianluca, Cuneo, Lisa, Banerjee, Abhirup, Xing, Zhaohu, Zhu, Lei, Mesbah, Zacharia, Jain, Dhruv, Mayet, Tsiry, Yuan, Hongyu, Lyu, Qing, Qayyum, Abdul, Mazher, Moona, Wells, Athol, Walsh, Simon LF, Yang, Guang
Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway trees remains prohibitively time-consuming. While significant efforts have been made
Externí odkaz:
http://arxiv.org/abs/2312.13752
Autor:
Fang, Yingying, Wu, Shuang, Zhang, Sheng, Huang, Chaoyan, Zeng, Tieyong, Xing, Xiaodan, Walsh, Simon, Yang, Guang
Effectively leveraging multimodal data such as various images, laboratory tests and clinical information is gaining traction in a variety of AI-based medical diagnosis and prognosis tasks. Most existing multi-modal techniques only focus on enhancing
Externí odkaz:
http://arxiv.org/abs/2311.01066
We propose a novel Deep Active Learning (DeepAL) model-3D Wasserstein Discriminative UNet (WD-UNet) for reducing the annotation effort of medical 3D Computed Tomography (CT) segmentation. The proposed WD-UNet learns in a semi-supervised way and accel
Externí odkaz:
http://arxiv.org/abs/2310.05638
Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools. These tools were intended to alleviate the burden on healthcare systems, control the rapid spread o
Externí odkaz:
http://arxiv.org/abs/2311.06258
Training medical AI algorithms requires large volumes of accurately labeled datasets, which are difficult to obtain in the real world. Synthetic images generated from deep generative models can help alleviate the data scarcity problem, but their effe
Externí odkaz:
http://arxiv.org/abs/2305.09789