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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

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Quanzheng Li

    1. Harvard Medical School, Boston, USA

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  2. Richard Leahy

    1. University of Southern California, Los Angeles, USA

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  3. Bin Dong

    1. Peking University, Beijing, China

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  4. Xiang Li

    1. Harvard Medical School, Boston, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11977)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: MMMI 2019.

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Table of contents (13 papers)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Multi-modal Image Prediction via Spatial Hybrid U-Net

    • Akib Zaman, Lu Zhang, Jingwen Yan, Dajiang Zhu
    Pages 1-9

  3. Automatic Segmentation of Liver CT Image Based on Dense Pyramid Network

    • Hongli Xu, Binhua Wang, Wanguo Xue, Yao Zhang, Cheng Zhong, Yongliang Chen et al.
    Pages 10-16

  4. OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images

    • Yu Chen, Jiawei Chen, Dong Wei, Yuexiang Li, Yefeng Zheng
    Pages 17-25

  5. Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data

    • Ning Qiang, Bao Ge, Qinglin Dong, Fangfei Ge, Tianming Liu
    Pages 26-34

  6. Feature Pyramid Based Attention for Cervical Image Classification

    • Hongfeng Li, Jian Zhao, Li Zhang, Jie Zhao, Li Yang, Quanzheng Li
    Pages 35-42

  7. Single-Scan Dual-Tracer Separation Network Based on Pre-trained GRU

    • Junyi Tong, Yunmei Chen, Huafeng Liu
    Pages 43-50

  8. PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation

    • Jie Zhao, Lei Dai, Mo Zhang, Fei Yu, Meng Li, Hongfeng Li et al.
    Pages 51-58

  9. Automated Classification of Arterioles and Venules for Retina Fundus Images Using Dual Deeply-Supervised Network

    • Meng Li, Jie Zhao, Yan Zhang, Danmei He, Jinqiong Zhou, Jia Jia et al.
    Pages 59-67

  10. Liver Segmentation from Multimodal Images Using HED-Mask R-CNN

    • Supriti Mulay, G. Deepika, S. Jeevakala, Keerthi Ram, Mohanasankar Sivaprakasam
    Pages 68-75

  11. aEEG Signal Analysis with Ensemble Learning for Newborn Seizure Detection

    • Yini Pan, Hongfeng Li, Lili Liu, Quanzheng Li, Xinlin Hou, Bin Dong
    Pages 76-84

  12. Speckle Noise Removal in Ultrasound Images Using a Deep Convolutional Neural Network and a Specially Designed Loss Function

    • Danlei Feng, Weichen Wu, Hongfeng Li, Quanzheng Li
    Pages 85-92

  13. Automatic Sinus Surgery Skill Assessment Based on Instrument Segmentation and Tracking in Endoscopic Video

    • Shan Lin, Fangbo Qin, Randall A. Bly, Kris S. Moe, Blake Hannaford
    Pages 93-100

  14. U-Net Training with Instance-Layer Normalization

    • Xiao-Yun Zhou, Peichao Li, Zhao-Yang Wang, Guang-Zhong Yang
    Pages 101-108

  15. Back Matter

    Pages 109-109
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  1. Multiscale Multimodal Medical Imaging

Keywords

About this book

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.

Editors and Affiliations

  • Harvard Medical School, Boston, USA

    Quanzheng Li, Xiang Li

  • University of Southern California, Los Angeles, USA

    Richard Leahy

  • Peking University, Beijing, China

    Bin Dong

Bibliographic Information

  • Book Title: Multiscale Multimodal Medical Imaging

  • Book Subtitle: First International Workshop, MMMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings

  • Editors: Quanzheng Li, Richard Leahy, Bin Dong, Xiang Li

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-37969-8

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2020

  • Softcover ISBN: 978-3-030-37968-1Published: 20 December 2019

  • eBook ISBN: 978-3-030-37969-8Published: 19 December 2019

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: X, 109

  • Number of Illustrations: 9 b/w illustrations, 46 illustrations in colour

  • Topics: Image Processing and Computer Vision, Machine Learning, Pattern Recognition

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