Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018; 21st International C Alejandro F. Frangi,Julia A. Schnabel,Gabor

乳白光

Can Deep Learning Relax Endomicroscopy Hardware Miniaturization Requirements?e been made to develop miniaturized . imaging devices, specifically confocal laser microscopes, for both clinical and research applications. However, current implementations of miniature CLE components such as confocal lenses compromise image resolution, signal-to-noise ratio, or both, which negativ

Latency

大方不好

notion

A No-Reference Quality Metric for Retinal Vessel Tree Segmentationios, the ability to automatically assess the quality of predictions when no expert annotation is available can be critical. In this paper, we propose a new method for quality assessment of retinal vessel tree segmentations in the absence of a reference ground-truth. For this, we artificially degrade

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Pillory

A Deep Learning Based Anti-aliasing Self Super-Resolution Algorithm for MRIio requires a long time, making them costly and susceptible to motion artifacts. A common way to partly achieve this goal is to acquire MR images with good in-plane resolution and poor through-plane resolution?(i.e., large slice thickness). For such 2D imaging protocols, aliasing is also introduced

怕失去錢(qián)

臆斷

Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Imagesarning. We exploit memory-efficient backpropagation techniques, to reduce the memory complexity of network training from being linear in the network’s depth, to being roughly constant – permitting us to elongate deep architectures with negligible memory increase. We evaluate our methodology in the p

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