Below find the call for papers for the workshop "Machine Learning and Computational Intelligence in multi-omics and medical image analysis (MALCI_MUOMI 2020)".
It will be held during the 2020 AIAI conference, collocated with EANN 2020, the 21st International Conference on engineering Applications of Neural Networks. This will be held 5-7 June 2020, Porto Carras Grand Resort, Halkidiki Greece.
**Important Dates**
Paper Submission Deadline: 29th of February 2020
Notification of Acceptance/Rejection: 22nd of March 2020
Camera Ready Submission/Registration: 2nd of April 2020
Early / Author registration by: 2nd of April 2020
Conference Dates: 5-7 June, 2020
**Link for submissions**
http://www.aiai2020.eu/malci_muomi2020/#submission
**Aim and scope**
There is an increasing need for the application of Machine Learning (ML) and Computational Intelligence (CI) techniques, which can effectively perform image processing operations (such as segmentation, co-registration, classification, and dimensionality
reduction), in the fields of neuroimaging and oncological imaging. Although the manual approach often remains the golden standard in some tasks (e.g., segmentation), ML can be exploited to automate and facilitate the work of researchers and clinicians. Frequently
used techniques include Support Vector Machines (SVMs) for classification problems, graph-based methods, and Artificial Neural Networks (ANNs).
More recently, deep ANNs have shown to be very successful in computer vision tasks owing to the ability to automatically extract hierarchical descriptive features from input images. It has also been used in the oncological and neuroimaging domains for
automatic disease diagnosis, tissue segmentation, and even synthetic image generation. The main issue, however, remains the relative sample paucity of the typical imaging datasets that leads to a poor generalisation of the employed deep ANNs, considering the
high number of required parameters. Consequently, parameter-efficient design paradigms, specifically tailored to medical applications, ought to be devised, also by exploiting CI-based techniques (e.g., neuroevolution).
In this context, these advanced ML techniques can be suitably exploited to combine heterogeneous sources of information, allowing for multi-omics data integration. Such a kind of analyses may represent a significant step towards personalised medicine.
Topics of interest include but are not limited to:
· ML techniques for segmentation, co-registration, classification, or dimensionality reduction of medical images
· Deep neural networks for medical image super-resolution, de-noising and synthesis
· Deep Learning for neuroimaging and oncological imaging analysis
· Integration of multi-omics data
· Brain network analysis
· Application of graph theory to MRI and functional MRI (fMRI) data
· Application of ML methods for neurodegenerative disease studies
· Computational modelling and analysis of neuroimaging
· Methods of analysis for structural or functional connectivity
· Development of new neuroimaging tools
· Radiomic analyses for tumour phenotypes
· Radiogenomics for intra- and inter-tumoural heterogeneity evaluation
· Generative adversarial models for data augmentation and image super-resolution
· CI methods for optimizing medical image analysis tasks
**Workshop Organizing Committee**
Tiago Azevedo Department of Computer Science and Technology , University of Cambridge, Cambridge, (UK)
Giovanna Maria Dimitri Department of Computer Science and Technology , University of Cambridge, Cambridge (UK), Department of Medicine, University of Siena (Italy)
Prof Pietro Liς Department of Computer Science and Technology , University of Cambridge, Cambridge, (UK)
Dr Leonardo Rundo Department of Radiology, University of Cambridge, Cambridge (UK )
Simeon Spasov Department of Computer Science and Technology , University of Cambridge, Cambridge (UK)
Dr Andrea Tangherloni Department of Haematology, University of Cambridge, Cambridge, (UK)
Jin Zhu Department of Computer Science and Technology , University of Cambridge, Cambridge, UK