PRedictive Intelligence in MEdicine

1. Front Matter

   Pages I-XII

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2. Computer Aided Identification of Motion Disturbances Related to Parkinson’s Disease

   • Gudmundur Einarsson, Line K. H. Clemmensen, Ditte Rudå, Anders Fink-Jensen, Jannik B. Nielsen, Anne Katrine Pagsberg et al.

   Pages 1-8

3. Prediction of Severity and Treatment Outcome for ASD from fMRI

   • Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li, Pamela Ventola, James S. Duncan

   Pages 9-17

4. Enhancement of Perivascular Spaces Using a Very Deep 3D Dense Network

   • Euijin Jung, Xiaopeng Zong, Weili Lin, Dinggang Shen, Sang Hyun Park

   Pages 18-25

5. Generation of Amyloid PET Images via Conditional Adversarial Training for Predicting Progression to Alzheimer’s Disease

   • Yu Yan, Hoileong Lee, Edward Somer, Vicente Grau

   Pages 26-33

6. Prediction of Hearing Loss Based on Auditory Perception: A Preliminary Study

   • Muhammad Ilyas, Alice Othmani, Amine Nait-Ali

   Pages 34-41

7. Predictive Patient Care: Survival Model to Prevent Medication Non-adherence

   • T. Janssoone, P. Rinder, P. Hornus, D. Kanoun

   Pages 42-50

8. Joint Robust Imputation and Classification for Early Dementia Detection Using Incomplete Multi-modality Data

   • Kim-Han Thung, Pew-Thian Yap, Dinggang Shen

   Pages 51-59

9. Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer’s Disease

   • Adrià Casamitjana, Verónica Vilaplana, Paula Petrone, José Luis Molinuevo, Juan Domingo Gispert

   Pages 60-67

10. Predicting Nucleus Basalis of Meynert Volume from Compartmental Brain Segmentations

    • Hennadii Madan, Rok Berlot, Nicola J. Ray, Franjo Pernuš, Žiga Špiclin

    Pages 68-75

11. Multi-modal Neuroimaging Data Fusion via Latent Space Learning for Alzheimer’s Disease Diagnosis

    • Tao Zhou, Kim-Han Thung, Mingxia Liu, Feng Shi, Changqing Zhang, Dinggang Shen

    Pages 76-84

12. Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression Using Irregularity Age Map in Brain MRI

    • Muhammad Febrian Rachmadi, Maria del C. Valdés-Hernández, Taku Komura

    Pages 85-93

13. Multi-view Brain Network Prediction from a Source View Using Sample Selection via CCA-Based Multi-kernel Connectomic Manifold Learning

    • Minghui Zhu, Islem Rekik

    Pages 94-102

14. Predicting Emotional Intelligence Scores from Multi-session Functional Brain Connectomes

    • Anna Lisowska, Islem Rekik

    Pages 103-111

15. Predictive Modeling of Longitudinal Data for Alzheimer’s Disease Diagnosis Using RNNs

    • Maryamossadat Aghili, Solale Tabarestani, Malek Adjouadi, Ehsan Adeli

    Pages 112-119

16. Towards Continuous Health Diagnosis from Faces with Deep Learning

    • Victor Martin, Renaud Séguier, Aurélie Porcheron, Frédérique Morizot

    Pages 120-128

17. XmoNet: A Fully Convolutional Network for Cross-Modality MR Image Inference

    • Sophia Bano, Muhammad Asad, Ahmed E. Fetit, Islem Rekik

    Pages 129-137

18. 3D Convolutional Neural Network and Stacked Bidirectional Recurrent Neural Network for Alzheimer’s Disease Diagnosis

    • Chiyu Feng, Ahmed Elazab, Peng Yang, Tianfu Wang, Baiying Lei, Xiaohua Xiao

    Pages 138-146

19. Generative Adversarial Training for MRA Image Synthesis Using Multi-contrast MRI

    • Sahin Olut, Yusuf H. Sahin, Ugur Demir, Gozde Unal

    Pages 147-154

20. Diffusion MRI Spatial Super-Resolution Using Generative Adversarial Networks

    • Enes Albay, Ugur Demir, Gozde Unal

    Pages 155-163

This book constitutes the refereed proceedings of the First International Workshop on PRedictive Intelligence in MEdicine, PRIME 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018.

The 20 full papers presented were carefully reviewed and selected from 23 submissions. The main aim of the workshop is to propel the advent of predictive models in a broad sense, with application to medical data. Particularly, the workshop will admit papers describing new cutting-edge predictive models and methods that solve challenging problems in the medical field.

• Artificial intelligence
• Classification
• Classification accuracy
• Computer vision
• Feature selection
• Health informatics
• Image analysis
• Image and video acquisition
• Image processing
• Machine learning
• Medical technologies
• Neural Networks
• Pattern recognition
• Prediction
• Support Vector Machines (SVM)

• University of Dundee, Dundee, UK

  Islem Rekik

• Istanbul Technical University, Istanbul, Turkey

  Gozde Unal

• Stanford University, Stanford, USA

  Ehsan Adeli

• Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea (Republic of)

  Sang Hyun Park

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