神圣不可
发表于 2025-3-30 09:52:19
Learning Transferable 3D-CNN for MRI-Based Brain Disorder Classification from Scratch: An Empirical the . of 3D-CNNs to the transferability, and verify that fine-tuning CNNs can significantly enhance the transferability. This is different from the previous finding that fine-tuning CNNs (pretrained on ImageNet) cannot improve the model transferability in 2D medical image analysis. (3) We also stud
下级
发表于 2025-3-30 15:23:20
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惩罚
发表于 2025-3-30 18:41:58
Interpretable Histopathology Image Diagnosis via Whole Tissue Slide Level Supervision,atch automatically. More importantly, visualization of weight for each patch in a WSI demonstrates that our approach matches the concerns of pathologists. Furthermore, extensive experiments demonstrate the superiority of the interpretable dual encoder network.
indices
发表于 2025-3-30 21:20:09
Variational Encoding and Decoding for Hybrid Supervision of Registration Network,ns can be simulated to serve as the ground-truth for supervised learning of registration. By working alternatively with the conventional unsupervised training, our registration network can better adapt to shape variability and yield accurate and consistent deformations. Experiments on 3D brain magne
能得到
发表于 2025-3-31 03:48:41
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CT-angiography
发表于 2025-3-31 08:03:32
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欢腾
发表于 2025-3-31 10:35:45
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披肩
发表于 2025-3-31 14:26:05
Learning Structure from Visual Semantic Features and Radiology Ontology for Lymph Node Classificatidel on a T2 MRI image dataset with 821 samples and 14 types of lymph nodes. Although this dataset is very unbalanced on different types of lymph nodes, our model shows promising classification results on this challenging datasets compared to several state of art methods.
吞噬
发表于 2025-3-31 18:00:10
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Goblet-Cells
发表于 2025-3-31 22:17:37
StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis on a given modality and super-resolve brain graphs in both inter and intra domains. Our SG-Net is grounded in three main contributions: (i) predicting a target graph from a source one based on a novel graph generative adversarial network in both inter (e.g., morphological-functional) and intra (e.g