Cantankerous
发表于 2025-3-25 06:30:32
Capsule Networks Against Medical Imaging Data Challengeslving medical image classification problems is today a bottleneck for many applications. Recently, capsule networks were proposed to deal with shortcomings of Convolutional Neural Networks (ConvNets). In this work, we compare the behavior of capsule networks against ConvNets under typical datasets c
Toxoid-Vaccines
发表于 2025-3-25 08:24:52
Conference proceedings 2018 and Computer Assisted Stenting, CVII-STENT 2018, and the Third International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018,
SENT
发表于 2025-3-25 14:16:53
Automated Quantification of Blood Flow Velocity from Time-Resolved CT Angiography results are compared to values obtained through manual reading of the datasets and show good agreement. Based on a small patient study, we explore initial utility of these quantitative measures for the diagnosis of lower extremity PAD.
勋章
发表于 2025-3-25 18:48:51
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EXULT
发表于 2025-3-25 21:20:09
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SOBER
发表于 2025-3-26 01:50:07
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誓言
发表于 2025-3-26 06:36:44
Layer Separation in X-ray Angiograms for Vessel Enhancement with Fully Convolutional Networkresults show that the FCN trained with both losses can well enhance vessel structures by separating the vessel layer, while the . loss results in better contrast. In contrast to traditional layer separation methods [.], both our methods can be executed much faster and thus have potential for real-time applications.
尽责
发表于 2025-3-26 09:28:31
Deep Learning-Based Detection and Segmentation for BVS Struts in IVOCT Imagested 97.7% of 4029 BVS struts with 2.41% false positives. The average Dice coefficient between the segmented struts and ground truth was 0.809. It concludes that the proposed method is accurate and efficient for BVS struts detection and segmentation, and enables automatic malapposition analysis.
Brochure
发表于 2025-3-26 13:07:35
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侵略者
发表于 2025-3-26 19:13:17
Feature Learning Based on Visual Similarity Triplets in Medical Image Analysis: A Case Study of Emphusing CNNs. We evaluate the networks on 973 images, and show that the CNNs can learn disease relevant feature representations from derived similarity triplets. To our knowledge this is the first medical image application where similarity triplets has been used to learn a feature representation that can be used for embedding unseen test images.