Titlebook: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics; Le Lu,Xiaosong Wang,Lin Yang Book 2019 Sprin

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Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-Fine Framework and Its Adversarial Example last one contain healthy and pathological pancreases, respectively, and achieve the current state of the art in terms of Dice-Sørensen Coefficient (DSC) on all of them. Especially, on the NIH pancreas dataset, we outperform the previous best by an average of over ., and the worst case is improved

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Glaucoma Detection Based on Deep Learning Network in Fundus Imageided network, local disc region stream, and disc polar transformation stream. The DENet produces the glaucoma detection result from the image directly without segmentation. Finally, we compare two deep learning methods with other related methods on several glaucoma detection datasets.

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Anisotropic Hybrid Network for Cross-Dimension Transferable Feature Learning in 3D Medical Imageso 3D anisotropic volumes. Such a transfer inherits the desired strong generalization capability for within-slice information while naturally exploiting between-slice information for more effective modeling. We show the effectiveness of the 3D AH-Net on two example medical image analysis applications

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Tumor Growth Prediction Using Convolutional Networksn. We then present a two-stream ConvNets which directly model and learn the two fundamental processes of tumor growth, i.e., cell invasion and mass effect, and predict the subsequent involvement regions of a tumor. Experiments on a longitudinal pancreatic tumor data set show that both approaches sub

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