Machine Learning and Computational Intelligence in multi-omics and medical image analysis

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


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


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

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