Zobrazeno 1 - 10
of 413
pro vyhledávání: '"Wang, guotai"'
Head and neck tumors and metastatic lymph nodes are crucial for treatment planning and prognostic analysis. Accurate segmentation and quantitative analysis of these structures require pixel-level annotation, making automated segmentation techniques e
Externí odkaz:
http://arxiv.org/abs/2412.14846
Autor:
Song, Tao, Wu, Yicheng, Hu, Minhao, Luo, Xiangde, Luo, Guoting, Wang, Guotai, Guo, Yi, Xu, Feng, Zhang, Shaoting
Accelerated MRI reconstruction techniques aim to reduce examination time while maintaining high image fidelity, which is highly desirable in clinical settings for improving patient comfort and hospital efficiency. Existing deep learning methods typic
Externí odkaz:
http://arxiv.org/abs/2412.09998
Autor:
Song, Tao, Wu, Yicheng, Hu, Minhao, Luo, Xiangde, Wei, Linda, Wang, Guotai, Guo, Yi, Xu, Feng, Zhang, Shaoting
Multimodal MR image synthesis aims to generate missing modality image by fusing and mapping a few available MRI data. Most existing approaches typically adopt an image-to-image translation scheme. However, these methods often suffer from sub-optimal
Externí odkaz:
http://arxiv.org/abs/2411.14684
Autor:
Dorent, Reuben, Khajavi, Roya, Idris, Tagwa, Ziegler, Erik, Somarouthu, Bhanusupriya, Jacene, Heather, LaCasce, Ann, Deissler, Jonathan, Ehrhardt, Jan, Engelson, Sofija, Fischer, Stefan M., Gu, Yun, Handels, Heinz, Kasai, Satoshi, Kondo, Satoshi, Maier-Hein, Klaus, Schnabel, Julia A., Wang, Guotai, Wang, Litingyu, Wald, Tassilo, Yang, Guang-Zhong, Zhang, Hanxiao, Zhang, Minghui, Pieper, Steve, Harris, Gordon, Kikinis, Ron, Kapur, Tina
Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentation frameworks in medical imaging often rely on fully annotated dataset
Externí odkaz:
http://arxiv.org/abs/2408.10069
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2024)
Assessing the presence of potentially malignant lymph nodes aids in estimating cancer progression, and identifying surrounding benign lymph nodes can assist in determining potential metastatic pathways for cancer. For quantitative analysis, automatic
Externí odkaz:
http://arxiv.org/abs/2408.09411
Deep learning models have exhibited remarkable efficacy in accurately delineating the prostate for diagnosis and treatment of prostate diseases, but challenges persist in achieving robust generalization across different medical centers. Source-free D
Externí odkaz:
http://arxiv.org/abs/2407.02893
Deep learning has enabled great strides in abdominal multi-organ segmentation, even surpassing junior oncologists on common cases or organs. However, robustness on corner cases and complex organs remains a challenging open problem for clinical adopti
Externí odkaz:
http://arxiv.org/abs/2406.13674
Adapting a medical image segmentation model to a new domain is important for improving its cross-domain transferability, and due to the expensive annotation process, Unsupervised Domain Adaptation (UDA) is appealing where only unlabeled images are ne
Externí odkaz:
http://arxiv.org/abs/2404.04971
Despite that deep learning methods have achieved remarkable performance in pathology image classification, they heavily rely on labeled data, demanding extensive human annotation efforts. In this study, we present a novel human annotation-free method
Externí odkaz:
http://arxiv.org/abs/2403.15836
Autor:
Wang, Xiaosong, Zhang, Xiaofan, Wang, Guotai, He, Junjun, Li, Zhongyu, Zhu, Wentao, Guo, Yi, Dou, Qi, Li, Xiaoxiao, Wang, Dequan, Hong, Liang, Lao, Qicheng, Ruan, Tong, Zhou, Yukun, Li, Yixue, Zhao, Jie, Li, Kang, Sun, Xin, Zhu, Lifeng, Zhang, Shaoting
The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and many other research areas. However, domain-specific applications of s
Externí odkaz:
http://arxiv.org/abs/2402.18028