Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures : first International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, Octo

This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8 th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data. Front Matter ....Pages i-xvii Front Matter ....Pages 1-1 Probabilistic Surface Reconstruction with Unknown Correspondence (Dennis Madsen, Thomas Vetter, Marcel Lüthi)....Pages 3-11 Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty (Alireza Sedghi, Tina Kapur, Jie Luo, Parvin Mousavi, William M. Wells III)....Pages 12-22 Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference (Raghav Mehta, Thomas Christinck, Tanya Nair, Paul Lemaitre, Douglas Arnold, Tal Arbel)....Pages 23-32 Reg R-CNN: Lesion Detection and Grading Under Noisy Labels (Gregor N. Ramien, Paul F. Jaeger, Simon A. A. Kohl, Klaus H. Maier-Hein)....Pages 33-41 Fast Nonparametric Mutual-Information-based Registration and Uncertainty Estimation (Mikael Agn, Koen Van Leemput)....Pages 42-51 Quantifying Uncertainty of Deep Neural Networks in Skin Lesion Classification (Pieter Van Molle, Tim Verbelen, Cedric De Boom, Bert Vankeirsbilck, Jonas De Vylder, Bart Diricx et al.)....Pages 52-61 Front Matter ....Pages 63-63 A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Data (Min Zhang, Kevin H. Leung, Zili Ma, Jin Wen, Gopal Avinash)....Pages 65-74 Out of Distribution Detection for Intra-operative Functional Imaging (Tim J. Adler, Leonardo Ayala, Lynton Ardizzone, Hannes G. Kenngott, Anant Vemuri, Beat P. Müller-Stich et al.)....Pages 75-82 Front Matter ....Pages 83-83 A Clinical Measuring Platform for Building the Bridge Across the Quantification of Pathological N-Cells in Medical Imaging for Studies of Disease (Peifang Guo)....Pages 85-93 Spatiotemporal Statistical Model of Anatomical Landmarks on a Human Embryonic Brain (Aoi Shinjo, Atsushi Saito, Tetsuya Takakuwa, Shigehito Yamada, Hidekata Hontani, Hiroshi Matsuzoe et al.)....Pages 94-103 Spaciousness Filters for Non-contrast CT Volume Segmentation of the Intestine Region for Emergency Ileus Diagnosis (Hirohisa Oda, Kohei Nishio, Takayuki Kitasaka, Benjamin Villard, Hizuru Amano, Kosuke Chiba et al.)....Pages 104-114 Recovering Physiological Changes in Nasal Anatomy with Confidence Estimates (Ayushi Sinha, Xingtong Liu, Masaru Ishii, Gregory D. Hager, Russell H. Taylor)....Pages 115-124 Synthesis of Medical Images Using GANs (Luise Middel, Christoph Palm, Marius Erdt)....Pages 125-134 DPANet: A Novel Network Based on Dense Pyramid Feature Extractor and Dual Correlation Analysis Attention Modules for Colon Glands Segmentation (Shuting Liu, Baochang Zhang, Xi Li, Yiqing Liu, Mengying Hu, Tian Guan et al.)....Pages 135-145 Multi-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseases Using Ultrasound Imaging (Shi Yin, Qinmu Peng, Hongming Li, Zhengqiang Zhang, Xinge You, Hangfan Liu et al.)....Pages 146-154 Data Augmentation from Sketch (Debora Gil, Antonio Esteban-Lansaque, Sebastian Stefaniga, Mihail Gaianu, Carles Sanchez)....Pages 155-162 An Automated CNN-based 3D Anatomical Landmark Detection Method to Facilitate Surface-Based 3D Facial Shape Analysis (Ruobing Huang, Michael Suttie, J. Alison Noble)....Pages 163-171 A Device-Independent Novel Statistical Modeling for Cerebral TOF-MRA Data Segmentation (Baochang Zhang, Zonghan Wu, Shuting Liu, Shoujun Zhou, Na Li, Gang Zhao)....Pages 172-181 Three-Dimensional Face Reconstruction from Uncalibrated Photographs: Application to Early Detection of Genetic Syndromes (Liyun Tu, Antonio R. Porras, Araceli Morales, Daniel A. Perez, Gemma Piella, Federico Sukno et al.)....Pages 182-189 Back Matter ....Pages 191-192 "This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data" -- OhioLink Library Catalog