Dinggang Shen

Guorong Wu

Mert Sabuncu

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics Features self-contained chapters with a thorough literature review Assesses the development of future machine learning techniques and the further application of existing techniques

Machine Learning and Medical Imaging

Brent C. Munsell

Paul Laurienti

Leonardo Bonilha

This book constitutes the refereed proceedings of the First International Workshop on Connectomics in NeuroImaging, CNI 2017, held in conjunction with MICCAI 2017 in Quebec City, Canada, in September 2017. The 19 full papers presented were carefully reviewed and selected from 26 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies as well as in various neuroimaging applications.

First International Workshop, CNI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings

Connectomics in NeuroImaging

Yiqiang Zhan

Wenjia Bai

Gerard Sanroma

Pierrick Coupé

This book constitutes the refereed proceedings of the Third International Workshop on Patch-Based Techniques in Medical Images, Patch-MI 2017, which was held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 18 regular papers presented in this volume were carefully reviewed and selected from 26 submissions. The papers are organized in topical sections on multi-atlas segmentation; segmentation; Alzheimer’s disease; reconstruction, denoising, super-resolution; tumor, lesion; and classification, retrival.

Third International Workshop, Patch-MI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings

Patch-Based Techniques in Medical Imaging

Islem Rekik

Markus D. Schirmer

Ai Wern Chung

Brent Munsell

This book constitutes the refereed proceedings of the Second International Workshop on Connectomics in NeuroImaging, CNI 2018, held in conjunction with MICCAI 2018 in Granada, Spain, in September 2018. The 15 full papers presented were carefully reviewed and selected from 20 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies as well as in various neuroimaging applications.

Second International Workshop, CNI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings

Guorong Wu

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