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Multiscale Multimodal Medical Imaging: First International Workshop, MMMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 2020 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 109 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 46 Illustrations, color; 9 Illustrations, black and white; X, 109 p. 55 illus., 46 illus. in color., 1 Paperback / softback
  • Sari: Image Processing, Computer Vision, Pattern Recognition, and Graphics 11977
  • Ilmumisaeg: 20-Dec-2019
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 303037968X
  • ISBN-13: 9783030379681
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  • Formaat: Paperback / softback, 109 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 46 Illustrations, color; 9 Illustrations, black and white; X, 109 p. 55 illus., 46 illus. in color., 1 Paperback / softback
  • Sari: Image Processing, Computer Vision, Pattern Recognition, and Graphics 11977
  • Ilmumisaeg: 20-Dec-2019
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 303037968X
  • ISBN-13: 9783030379681
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.
Multi-modal Image Prediction via Spatial Hybrid U-Net
1(9)
Akib Zaman
Lu Zhang
Jingwen Yan
Dajiang Zhu
Automatic Segmentation of Liver CT Image Based on Dense Pyramid Network
10(7)
Hongli Xu
Binhua Wang
Wanguo Xue
Yao Zhang
Cheng Zhong
Yongliang Chen
Jianfeng Leng
OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images
17(9)
Yu Chen
Jiawei Chen
Dong Wei
Yuexiang Li
Yefeng Zheng
Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data
26(9)
Ning Qiang
Bao Ge
Qinglin Dong
Fangfei Ge
Tianming Liu
Feature Pyramid Based Attention for Cervical Image Classification
35(8)
Hongfeng Li
Jian Zhao
Li Zhang
Jie Zhao
Li Yang
Quanzheng Li
Single-Scan Dual-Tracer Separation Network Based on Pre-trained GRU
43(8)
Junyi Tong
Yunmei Chen
Huafeng Liu
PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation
51(8)
Jie Zhao
Lei Dai
Mo Zhang
Fei Yu
Meng Li
Hongfeng Li
Wenjia Wang
Li Zhang
Automated Classification of Arterioles and Venules for Retina Fundus Images Using Dual Deeply-Supervised Network
59(9)
Meng Li
Jie Zhao
Yan Zhang
Danmei He
Jinqiong Zhou
Jia Jia
Haicheng She
Quanzheng Li
Li Zhang
Liver Segmentation from Multimodal Images Using HED-Mask R-CNN
68(8)
Supriti Mulay
G. Deepika
S. Jeevakala
Keerthi Ram
Mohanasankar Sivaprakasam
aEEG Signal Analysis with Ensemble Learning for Newborn Seizure Detection
76(9)
Yini Pan
Hongfeng Li
Lili Liu
Quanzheng Li
Xinlin Hou
Bin Dong
Speckle Noise Removal in Ultrasound Images Using a Deep Convolutional Neural Network and a Specially Designed Loss Function
85(8)
Danlei Feng
Weichen Wu
Hongfeng Li
Quanzheng Li
Automatic Sinus Surgery Skill Assessment Based on Instrument Segmentation and Tracking in Endoscopic Video
93(8)
Shan Lin
Fangbo Qin
Randall A. Bly
Kris S. Moe
Blake Hannaford
U-Net Training with Instance-Layer Normalization
101(8)
Xiao-Yun Zhou
Peichao Li
Zhao-Yang Wang
Guang-Zhong Yang
Author Index 109