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Titlebook: Information Processing in Medical Imaging; 26th International C Albert C. S. Chung,James C. Gee,Siqi Bao Conference proceedings 2019 Spring

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Rudrasis Chakraborty,Jose Bouza,Jonathan Manton,Baba C. Vemuri
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Explicit Topological Priors for Deep-Learning Based Image Segmentation Using Persistent Homology by applying it to the problem of left-ventricle segmentation of cardiac MR images of subjects from the UK Biobank dataset, where we show that it improves segmentation performance in terms of topological correctness without sacrificing pixelwise accuracy.
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InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Predictionty during convolutions. We design filters with different kernel sizes to build our architecture. We show our disease prediction results on two publicly available datasets. Further, we provide insights on the behaviour of regular GCNs and our proposed model under varying input scenarios on simulated data.
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A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration our framework leads to a novel . layer. We propose a novel method to improve DNN .. Results on three large datasets show that our framework improves segmentation quality and calibration, and provides more realistic uncertainty estimates, over existing methods.
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