DL4BRAIN 2019 : CFP Special Session on Deep learning for brain data (DL4BRAIN) @ IJCNN 2019
Call For Papers
** Apologies for cross-posting** CFP [Deadline extended]:
Special Session on "Deep learning for brain data"
2019 International Joint Conference on Neural Networks (IJCNN)
July 14-19 2019, Budapest, Hungary
Paper submission: EXTENDED to 15 January 2019
Notification of acceptance: 28 February 2019
Aims and Scope:
Structural and Functional techniques to investigate brain, such as MRI, CT scan, fMRI, EEG, PET, are nowadays widely used both for basic research (for instance on cognition) or for clinical purposes (for instance diagnosis of brain based disorders). In the past two decades, scientists have tried to use these techniques to study brain functioning, to investigate human cognition, to assist the diagnosis of brain-based disorders, and to try to predict the prognosis of patients.
Unfortunately, the attempts to find a technique that achieve results with the potential to be translated to daily practice have not succeeded due to the presence of complex, distributed and subtle individual differences that are difficult to detect using standard statistical techniques.
Very recently, this research field witnessed an exponential increased interest in the application of Machine Learning (ML) methods, and in particular of Deep Learning (DL), to brain data to support researchers in the study of cognition and to support clinicians in the diagnosis and prognosis of brain-based disorders. To date, applications of ML/DL techniques to brain data is still an under-investigated field of research.
The aim of this special session is twofold: first, it provides a point of contact between scientists and researchers from the machine learning and medical communities (medicine, neuroscience, psychology, psychophysiology, etc.), encouraging a multidisciplinary view on open problems.
Second, it provides a forum to present original ideas, theories and novel applications of ML/DL to brain data, and to find solutions to open issues.
Topics that are of interest to this session include, but are not limited to:
- Presentation of new structural and functional brain data databases;
- Computer vision applied to MRI, fMRI, DTI, PET;
- Time series analysis applied to EEG;
- New advancement in Deep Learning algorithms for brain data;
- Application of Deep Learning to brain data to study cognition (e.g., attention, language, memory, decision making, problem solving, spatial abilities);
- Application of Deep Learning to brain data for clinical diagnosis and prognosis of psychiatric and neurologic disorders;
- Application of Deep Learning to brain data for identification of risk factors of neurologic and psychiatric disorders;
- Application of Deep Learning to evaluate the impact of inter-scanner variability on the results;
- Multicentric studies on brain MRI data;
- Integrating functional and structural information to enhance clinical diagnosis and prognosis;
- Integrating functional and structural information to enhance the understanding of cognitive processes;
- For paper guidelines please visit https://www.ijcnn.org/paper-submission-guidelines
- For submissions please select the single topic "S10. Deep learning for brain data" from the "S. SPECIAL SESSIONS" category as the main research topic on https://ieee-cis.org/conferences/ijcnn2019/upload.php
- Nicolò Navarin, University of Padova, Italy
- Cristina Scarpazza, University of Padova, Italy
- Merylin Monaro, University of Padova, Italy
For any enquire, please write to: nnavarin [at] math.unipd.it, cristina.scarpazza [at] unipd.it or merylin.monaro [at] unipd.it