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41#
發(fā)表于 2025-3-28 15:33:16 | 只看該作者
42#
發(fā)表于 2025-3-28 22:31:34 | 只看該作者
43#
發(fā)表于 2025-3-29 01:50:02 | 只看該作者
Weakly- and Semi-supervised Graph CNN for Identifying Basal Cell Carcinoma on Pathological Images,grating patch level information into the whole image level prediction. Also, it is often difficult to obtain sufficient high-quality patch labels such as pixel-wise segmentation masks. Benefiting from the recent development of Graph-CNN (GCN), we propose a new weakly- and semi-supervised GCN archite
44#
發(fā)表于 2025-3-29 06:14:54 | 只看該作者
45#
發(fā)表于 2025-3-29 09:56:58 | 只看該作者
Automatic Detection of Craniomaxillofacial Anatomical Landmarks on CBCT Images Using 3D Mask R-CNN,the complexity of facial structure and the deformities of CMF patients, it is still difficult to accurately localize a large scale of landmarks simultaneously. In this work, we propose a three-stage coarse-to-fine deep learning method for digitizing 105 anatomical craniomaxillofacial landmarks on co
46#
發(fā)表于 2025-3-29 14:04:52 | 只看該作者
47#
發(fā)表于 2025-3-29 19:38:24 | 只看該作者
48#
發(fā)表于 2025-3-29 21:49:05 | 只看該作者
OCD Diagnosis via Smoothing Sparse Network and Stacked Sparse Auto-Encoder Learning,move redundant information in resting-state functional magnetic resonance imaging (rs-fMRI) data via the brain functional connectivity network (BFCN) and retain good biological characteristics, it is an important method for OCD analysis. However, most existing methods ignore the relationship among s
49#
發(fā)表于 2025-3-30 01:19:31 | 只看該作者
50#
發(fā)表于 2025-3-30 06:42:38 | 只看該作者
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