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Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education
Yuanji Zhang, Yuhao Huang, Haoran Dou +28
Orofacial clefts are among the most common congenital craniofacial abnormalities, yet accurate prenatal detection remains challenging due to the scarcity of experienced specialists…
Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound
Yuhao Huang, Ao Chang, Haoran Dou +8
Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, d…
Robust Box Prompt based SAM for Medical Image Segmentation
Yuhao Huang, Xin Yang, Han Zhou +4
The Segment Anything Model (SAM) can achieve satisfactory segmentation performance under high-quality box prompts. However, SAM's robustness is compromised by the decline in box qu…
A Foundation Model for General Moving Object Segmentation in Medical Images
Zhongnuo Yan, Tong Han, Yuhao Huang +7
Medical image segmentation aims to delineate the anatomical or pathological structures of interest, playing a crucial role in clinical diagnosis. A substantial amount of high-quali…
Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training
Jian Wang, Xin Yang, Xiaohong Jia +9
Thyroid nodule classification and segmentation in ultrasound images are crucial for computer-aided diagnosis; however, they face limitations owing to insufficient labeled data. In…