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pro vyhledávání: '"Ni, Dong"'
Fine-grained spatio-temporal learning is crucial for freehand 3D ultrasound reconstruction. Previous works mainly resorted to the coarse-grained spatial features and the separated temporal dependency learning and struggles for fine-grained spatio-tem
Externí odkaz:
http://arxiv.org/abs/2407.04242
Prostate cancer (PCa) poses a significant threat to men's health, with early diagnosis being crucial for improving prognosis and reducing mortality rates. Transrectal ultrasound (TRUS) plays a vital role in the diagnosis and image-guided intervention
Externí odkaz:
http://arxiv.org/abs/2407.00678
Echocardiography (ECHO) video is widely used for cardiac examination. In clinical, this procedure heavily relies on operator experience, which needs years of training and maybe the assistance of deep learning-based systems for enhanced accuracy and e
Externí odkaz:
http://arxiv.org/abs/2406.14098
UniUSNet: A Promptable Framework for Universal Ultrasound Disease Prediction and Tissue Segmentation
Ultrasound is a widely used imaging modality in clinical practice due to its low cost, portability, and safety. Current research in general AI for healthcare focuses on large language models and general segmentation models, with insufficient attentio
Externí odkaz:
http://arxiv.org/abs/2406.01154
Deformable image registration plays a crucial role in medical imaging, aiding in disease diagnosis and image-guided interventions. Traditional iterative methods are slow, while deep learning (DL) accelerates solutions but faces usability and precisio
Externí odkaz:
http://arxiv.org/abs/2403.16526
Bandwidth constraints during signal acquisition frequently impede real-time detection applications. Hyperspectral data is a notable example, whose vast volume compromises real-time hyperspectral detection. To tackle this hurdle, we introduce a novel
Externí odkaz:
http://arxiv.org/abs/2403.01412
Autor:
Wang, Jian, Yang, Xin, Jia, Xiaohong, Xue, Wufeng, Chen, Rusi, Chen, Yanlin, Zhu, Xiliang, Liu, Lian, Cao, Yan, Zhou, Jianqiao, Ni, Dong, Gu, Ning
Thyroid nodule classification and segmentation in ultrasound images are crucial for computer-aided diagnosis; however, they face limitations owing to insufficient labeled data. In this study, we proposed a multi-view contrastive self-supervised metho
Externí odkaz:
http://arxiv.org/abs/2402.11497
Different diseases, such as histological subtypes of breast lesions, have severely varying incidence rates. Even trained with substantial amount of in-distribution (ID) data, models often encounter out-of-distribution (OOD) samples belonging to unsee
Externí odkaz:
http://arxiv.org/abs/2402.07452
Publikováno v:
Ultrasound in Medicine & Biology, Volume 50, Issue 2, February 2024, Pages 304-314
Objective: Ultrasound (US) examination has unique advantages in diagnosing carpal tunnel syndrome (CTS) while identifying the median nerve (MN) and diagnosing CTS depends heavily on the expertise of examiners. To alleviate this problem, we aimed to d
Externí odkaz:
http://arxiv.org/abs/2402.05554
Breast lesion segmentation from breast ultrasound (BUS) videos could assist in early diagnosis and treatment. Existing video object segmentation (VOS) methods usually require dense annotation, which is often inaccessible for medical datasets. Further
Externí odkaz:
http://arxiv.org/abs/2402.04921