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{ "item_title" : "Multiscale Multimodal Medical Imaging", "item_author" : [" Quanzheng Li", "Richard Leahy", "Bin Dong "], "item_description" : "Multi-Modal Image Prediction via Spatial Hybrid U-Net.- Automatic Segmentation of Liver CT Image Based on Dense Pyramid Network.- OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images.- Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data.- Feature Pyramid based Attention for Cervical Image Classification.- Single-scan Dual-tracer Separation Network Based on Pre-trained GRU.- PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation.- Automated Classification of Arterioles and Venules for Retina Fundus Images using Dual Deeply-Supervised Network.- Liver Segmentation from Multimodal Images using HED-Mask R-CNN.- aEEG Signal Analysis with Ensemble Learning for Newborn Seizure Detection.- Speckle Noise Removal in Ultrasound Images Using A Deep Convolutional Neural Network and A Specially Designed Loss Function.- Automatic Sinus Surgery Skill Assessment Based on Instrument Segmentation and Tracking in Endoscopic Video.- U-Net Training with Instance-Layer Normalization.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/3/03/037/968/303037968X_b.jpg", "price_data" : { "retail_price" : "54.99", "online_price" : "54.99", "our_price" : "54.99", "club_price" : "54.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Multiscale Multimodal Medical Imaging|Quanzheng Li

Multiscale Multimodal Medical Imaging : First International Workshop, MMMI 2019, Held in Conjunction with Miccai 2019, Shenzhen, China, October 13, 201

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Overview

Multi-Modal Image Prediction via Spatial Hybrid U-Net.- Automatic Segmentation of Liver CT Image Based on Dense Pyramid Network.- OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images.- Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data.- Feature Pyramid based Attention for Cervical Image Classification.- Single-scan Dual-tracer Separation Network Based on Pre-trained GRU.- PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation.- Automated Classification of Arterioles and Venules for Retina Fundus Images using Dual Deeply-Supervised Network.- Liver Segmentation from Multimodal Images using HED-Mask R-CNN.- aEEG Signal Analysis with Ensemble Learning for Newborn Seizure Detection.- Speckle Noise Removal in Ultrasound Images Using A Deep Convolutional Neural Network and A Specially Designed Loss Function.- Automatic Sinus Surgery Skill Assessment Based on Instrument Segmentation and Tracking in Endoscopic Video.- U-Net Training with Instance-Layer Normalization.

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Details

  • ISBN-13: 9783030379681
  • ISBN-10: 303037968X
  • Publisher: Springer
  • Publish Date: December 2019
  • Dimensions: 9.21 x 6.14 x 0.25 inches
  • Shipping Weight: 0.39 pounds
  • Page Count: 109

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