Workshop

Machine Learning and Computational Intelligence in multi-omics and medical image analysis
(MALCI_MUOMI 2020)

Aims 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

Program

The program will be mainly based on the presentations of accepted papers, in line with workshops schedules of the previous edition of the conference.

Workshop Organizing Committee and Affiliations

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 Liò – 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

Submission

Submissions for the MALCI_MUOMI 2020 workshop will be received through the online system used for the main conference.

How to submit a contribution

Important: While visiting EasyAcademia.org, please use Firefox or Chrome. Internet Explorer or other browsers may have compatibility issues which can prevent you from submitting.

  • Create an “EasyAcademia” account through www.easyacademia.org
  • Activate your account by clicking on the activation link sent to your email.
    Note: Please check your Spam folder if you have not received the email within a few minutes.
  • Log into www.easyacademia.org/aiai2020, using the login details you provided at the beginning.
  • Click on Start a new submission on the top right to enter the submission process.
  • Select the appropriate track e.g. (Workshop – MALCI_MUOMI 2020)
  • Go through the guidelines
  • Enter the appropriate information in the Title step.
  • Under Authors please input details for each author of the paper. At least one author must be marked for each type of role available (presenter, corresponding).
    Note: Only authors marked as correspondents will receive updates and information regarding the submission.
  • In the Upload step, please click on Upload Paper and find the relevant completed document for your submission on your computer (PDF).
  • In the Attachment step, click on the Upload Attachment button on the right and find the Source file for your submission on your computer. Word files or a ZIP containing LaTeX files are accepted.
  • Under Summary you may check the details of the submission. If you wish to go back to a section in order to change details, just click on the appropriate step on the left.
  • If you are happy with the summary information, please click on Submit Now to finalize the process. A notification e-mail will be sent to the correspondents.

If at any point you are having trouble submitting, or require more information, please contact us at support@easyconferences.org and we will respond back as soon as possible.