Machine Learning in Medical Imaging

1. Front Matter

   Pages i-xviii

   PDF

2. Brain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization

   • Xuhua Ren, Lichi Zhang, Dongming Wei, Dinggang Shen, Qian Wang

   Pages 1-8

3. Spatial Regularized Classification Network for Spinal Dislocation Diagnosis

   • Bolin Lai, Shiqi Peng, Guangyu Yao, Ya Zhang, Xiaoyun Zhang, Yanfeng Wang et al.

   Pages 9-17

4. Globally-Aware Multiple Instance Classifier for Breast Cancer Screening

   • Yiqiu Shen, Nan Wu, Jason Phang, Jungkyu Park, Gene Kim, Linda Moy et al.

   Pages 18-26

5. Advancing Pancreas Segmentation in Multi-protocol MRI Volumes Using Hausdorff-Sine Loss Function

   • Hykoush Asaturyan, E. Louise Thomas, Julie Fitzpatrick, Jimmy D. Bell, Barbara Villarini

   Pages 27-35

6. WSI-Net: Branch-Based and Hierarchy-Aware Network for Segmentation and Classification of Breast Histopathological Whole-Slide Images

   • Haomiao Ni, Hong Liu, Kuansong Wang, Xiangdong Wang, Xunjian Zhou, Yueliang Qian

   Pages 36-44

7. Lesion Detection with Deep Aggregated 3D Contextual Feature and Auxiliary Information

   • Han Zhang, Albert C. S. Chung

   Pages 45-53

8. MSAFusionNet: Multiple Subspace Attention Based Deep Multi-modal Fusion Network

   • Sen Zhang, Changzheng Zhang, Lanjun Wang, Cixing Li, Dandan Tu, Rui Luo et al.

   Pages 54-62

9. DCCL: A Benchmark for Cervical Cytology Analysis

   • Changzheng Zhang, Dong Liu, Lanjun Wang, Yaoxin Li, Xiaoshi Chen, Rui Luo et al.

   Pages 63-72

10. Smartphone-Supported Malaria Diagnosis Based on Deep Learning

    • Feng Yang, Hang Yu, Kamolrat Silamut, Richard J. Maude, Stefan Jaeger, Sameer Antani

    Pages 73-80

11. Children’s Neuroblastoma Segmentation Using Morphological Features

    • Shengyang Li, Xiaoyun Zhang, Xiaoxia Wang, Yumin Zhong, Xiaofen Yao, Ya Zhang et al.

    Pages 81-88

12. GFD Faster R-CNN: Gabor Fractal DenseNet Faster R-CNN for Automatic Detection of Esophageal Abnormalities in Endoscopic Images

    • Noha Ghatwary, Massoud Zolgharni, Xujiong Ye

    Pages 89-97

13. Deep Active Lesion Segmentation

    • Ali Hatamizadeh, Assaf Hoogi, Debleena Sengupta, Wuyue Lu, Brian Wilcox, Daniel Rubin et al.

    Pages 98-105

14. Infant Brain Deformable Registration Using Global and Local Label-Driven Deep Regression Learning

    • Shunbo Hu, Lintao Zhang, Guoqiang Li, Mingtao Liu, Deqian Fu, Wenyin Zhang

    Pages 106-114

15. A Relation Hashing Network Embedded with Prior Features for Skin Lesion Classification

    • Wenbo Zheng, Chao Gou, Lan Yan

    Pages 115-123

16. End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation

    • Jinzheng Cai, Yingda Xia, Dong Yang, Daguang Xu, Lin Yang, Holger Roth

    Pages 124-132

17. Privacy-Preserving Federated Brain Tumour Segmentation

    • Wenqi Li, Fausto Milletarì, Daguang Xu, Nicola Rieke, Jonny Hancox, Wentao Zhu et al.

    Pages 133-141

18. Residual Attention Generative Adversarial Networks for Nuclei Detection on Routine Colon Cancer Histology Images

    • Junwei Li, Wei Shao, Zhongnian Li, Weida Li, Daoqiang Zhang

    Pages 142-150

19. Semi-supervised Multi-task Learning with Chest X-Ray Images

    • Abdullah-Al-Zubaer Imran, Demetri Terzopoulos

    Pages 151-159

20. Novel Bi-directional Images Synthesis Based on WGAN-GP with GMM-Based Noise Generation

    • Wei Huang, Mingyuan Luo, Xi Liu, Peng Zhang, Huijun Ding, Dong Ni

    Pages 160-168

This book constitutes the proceedings of the 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. 

The 78 papers presented in this volume were carefully reviewed and selected from 158 submissions. 
They focus on major trends and challenges in the area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc. 

• artificial intelligence
• automatic segmentations
• ct image
• image analysis
• image reconstruction
• image registration
• image segmentation
• learning algorithms
• medical images
• neural networks
• recurrent neural networks
• segmentation methods
• Support Vector Machines (SVM)

• Korea University, Seoul, Korea (Republic of)

  Heung-Il Suk

• University of North Carolina, Chapel Hill, USA

  Mingxia Liu, Chunfeng Lian

• Rensselaer Polytechnic Institute, Troy, USA

  Pingkun Yan

Policies and ethics