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E-grāmata: Understanding and Interpreting Machine Learning in Medical Image Computing Applications: First International Workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings

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  • Formāts: EPUB+DRM
  • Sērija : Lecture Notes in Computer Science 11038
  • Izdošanas datums: 23-Oct-2018
  • Izdevniecība: Springer Nature Switzerland AG
  • Valoda: eng
  • ISBN-13: 9783030026288
  • Formāts - EPUB+DRM
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  • Formāts: EPUB+DRM
  • Sērija : Lecture Notes in Computer Science 11038
  • Izdošanas datums: 23-Oct-2018
  • Izdevniecība: Springer Nature Switzerland AG
  • Valoda: eng
  • ISBN-13: 9783030026288

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This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018.

The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis.  


First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018
Alzheimer's Disease Modelling and Staging Through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes
3(12)
Clement Abi Nader
Nicholas Ayache
Philippe Robert
Marco Lorenzi
For the Alzheimer's Disease Neuroimaging Initiative
Multi-channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease
15(9)
Luigi Antelmi
Nicholas Ayache
Philippe Robert
Marco Lorenzi
For the Alzheimer's Disease Neuroimaging Initiative
Visualizing Convolutional Networks for MRI-Based Diagnosis of Alzheimer's Disease
24(8)
Johannes Rieke
Fabian Eitel
Martin Weygandt
John-Dylan Haynes
Kerstin Ritter
Finding Effective Ways to (Machine) Learn fMRI-Based Classifiers from Multi-site Data
32(11)
Roberto Vega
Russ Greiner
First International Workshop on Deep Learning Fails Workshop, DLF 2018
Towards Robust CT-Ultrasound Registration Using Deep Learning Methods
43(9)
Yuanyuan Sun
Adriaan Moelker
Wiro J. Niessen
Theo van Walsum
To Learn or Not to Learn Features for Deformable Registration?
52(9)
Aabhas Majumdar
Raghav Mehta
Jayanthi Sivaswamy
Evaluation of Strategies for PET Motion Correction - Manifold Learning vs. Deep Learning
61(9)
James R. Clough
Daniel R. Balfour
Claudia Prieto
Andrew J. Reader
Paul K. Marsden
Andrew P. King
Exploring Adversarial Examples: Patterns of One-Pixel Attacks
70(9)
David Kugler
Alexander Distergoft
Arjan Kuijper
Anirban Mukhopadhyay
Shortcomings of Ventricle Segmentation Using Deep Convolutional Networks
79(8)
Muhan Shoo
Shuo Han
Aaron Carass
Xiang Li
Ari M. Blitz
Jerry L. Prince
Lotta M. Ellingsen
Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks
87(10)
Saeid Asgari Taghanaki
Arkadeep Das
Ghassan Hamarneh
First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018
Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images
97(9)
Noel C. F. Codella
Chung-Ching Lin
Allan Halpern
Michael Hind
Rogerio Feris
John R. Smith
Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessment
106(9)
Sergio Pereira
Raphael Meier
Victor Alves
Mauricio Reyes
Carlos A. Silva
Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification
115(9)
Pieter Van Molle
Miguel De Strooper
Tim Verbelen
Bert Vankeirsbilck
Pieter Simoens
Bart Dhoedt
Regression Concept Vectors for Bidirectional Explanations in Histopathology
124(9)
Mara Graziani
Vincent Andrearczyk
Henning Muller
Towards Complementary Explanations Using Deep Neural Networks
133(8)
Wilson Silva
Kelwin Fernandes
Maria J. Cardoso
Jaime S. Cardoso
How Users Perceive Content-Based Image Retrieval for Identifying Skin Images
141(8)
Mahya Sadeghi
Parmit K. Chilana
M. Stella Atkins
Author Index 149