Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 1st edition by Danail Stoyanov, Zeike Taylor, Gustavo Carneiro, Tanveer Syeda-Mahmood 3030008886 9783030008888

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 1st edition by Danail Stoyanov, Zeike Taylor, Gustavo Carneiro, Tanveer Syeda-Mahmood – Ebook PDF Instant Download/DeliveryISBN:  3030008886, 9783030008888 

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Product details:

ISBN-10 :  3030008886 

ISBN-13 :  9783030008888

Author:  Danail Stoyanov, Zeike Taylor, Gustavo Carneiro, Tanveer Syeda-Mahmood 

This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 1st Table of contents:

1. 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018
2. UNet++: A Nested U-Net Architecture for Medical Image Segmentation
3. Deep Semi-supervised Segmentation with Weight-Averaged Consistency Targets
4. Handling Missing Annotations for Semantic Segmentation with Deep ConvNets
5. A Unified Framework Integrating Recurrent Fully-Convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data
6. Multi-scale Residual Network with Two Channels of Raw CT Image and Its Differential Excitation Component for Emphysema Classification
7. TreeNet: Multi-loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees
8. 3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation
9. Automatic Detection of Patients with a High Risk of Systolic Cardiac Failure in Echocardiography
10. MTMR-Net: Multi-task Deep Learning with Margin Ranking Loss for Lung Nodule Analysis
11. Active Deep Learning with Fisher Information for Patch-Wise Semantic Segmentation
12. Contextual Additive Networks to Efficiently Boost 3D Image Segmentations
13. Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration
14. Rapid Training Data Generation for Tissue Segmentation Using Global Approximate Block-Matching with Self-organizing Maps
15. Focal Dice Loss and Image Dilation for Brain Tumor Segmentation
16. Deep Particle Tracker: Automatic Tracking of Particles in Fluorescence Microscopy Images Using Deep Learning
17. 3D Convolutional Neural Networks for Classification of Functional Connectomes
18. Segmentation of Head and Neck Organs-At-Risk in Longitudinal CT Scans Combining Deformable Registrations and Convolutional Neural Networks
19. Unpaired Deep Cross-Modality Synthesis with Fast Training
20. UOLO – Automatic Object Detection and Segmentation in Biomedical Images
21. Unpaired Brain MR-to-CT Synthesis Using a Structure-Constrained CycleGAN
22. Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy
23. Weakly Supervised Localisation for Fetal Ultrasound Images

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Tags: Deep Learning, Medical Image, Analysis, Multimodal Learning, Clinical Decision Support, Danail Stoyanov, Zeike Taylor, Gustavo Carneiro, Tanveer Syeda Mahmood