Multiscale Multimodal Medical Imaging : First International Workshop, MMMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / edited by Quanzheng Li, Richard Leahy, Bin Dong, Xiang Li.

Format:

Book

Contributor:

Li, Quanzheng, Editor.

Leahy, Richard., Editor.

Dong, Bin, Editor.

Li, Xiang, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11977

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11977

Language:

English

Subjects (All):

Computer vision.

Machine learning.

Pattern recognition systems.

Computer Vision.

Machine Learning.

Automated Pattern Recognition.

Local Subjects:

Computer Vision.

Machine Learning.

Automated Pattern Recognition.

Physical Description:

1 online resource (X, 109 pages) : 55 illustrations, 46 illustrations in color.

Edition:

1st ed. 2020.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

This book constitutes the refereed proceedings of the First International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 13 papers presented were carefully reviewed and selected from 18 submissions. The MMMI workshop aims to advance the state of the art in multi-scale multi-modal medical imaging, including algorithm development, implementation of methodology, and experimental studies. The papers focus on medical image analysis and machine learning, especially on machine learning methods for data fusion and multi-score learning.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-030-37969-8

9783030379698

Access Restriction:

Restricted for use by site license.