Titlebook: Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data a; 7th Joint Internatio Danail Stoyanov,

Cantankerous

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

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

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

EXULT

SOBER

誓言

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

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

侵略者

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.