Titlebook: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support; 4th International Wo Danail Stoyanov,Zeike T

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Deep Particle Tracker: Automatic Tracking of Particles in Fluorescence Microscopy Images Using Deep We evaluated the performance of our approach using image data of the ISBI Particle Tracking Challenge as well as real fluorescence microscopy image sequences of virus structures. It turned out that the proposed approach outperforms previous methods.

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Some Nitrogen-Containing Compoundsd encoder-decoder network where the encoder and decoder sub-networks are connected through a series of nested, dense skip pathways. The re-designed skip pathways aim at reducing the semantic gap between the feature maps of the encoder and decoder sub-networks. We argue that the optimizer would deal

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https://doi.org/10.1007/978-1-4684-1833-0important classification benchmarks. In this work, we expand the Mean Teacher approach to segmentation tasks and show that it can bring important improvements in a realistic small data regime using a publicly available multi-center dataset from the Magnetic Resonance Imaging (MRI) domain. We also de

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Some Oxygen-Containing Compoundsanch direction estimation as only a by-product of vesselness or tubularness computation. In this work, we propose a deep learning framework for predicting tracking directions of anatomical tree structures. We modify the deep V-Net architecture with extra layers and leverage a novel multi-loss functi