[et_pb_section fb_built=”1″ _builder_version=”3.26.6″ background_image=”https:\/\/easyconferences.eu\/aiai2020\/wp-content\/uploads\/2019\/09\/1.jpg” global_module=”371″ saved_tabs=”all”][et_pb_row _builder_version=”3.26.6″][et_pb_column type=”4_4″ _builder_version=”3.26.6″][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”3.26.6″][et_pb_row _builder_version=”3.26.6″][et_pb_column type=”4_4″ _builder_version=”3.26.6″][et_pb_text _builder_version=”3.26.6″]<\/p>\n
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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).<\/p>\n
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).<\/p>\n
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.<\/p>\n
Topics of interest include but are not limited to:<\/p>\n
The program will be mainly based on the presentations of accepted papers, in line with workshops schedules of the previous edition of the conference.<\/p>\n
Tiago Azevedo<\/strong> \u2013 Department of Computer Science and Technology , University of Cambridge, Cambridge, (UK)<\/p>\n Giovanna Maria Dimitri <\/strong>\u2013 Department of Computer Science and Technology , University of Cambridge, Cambridge (UK), Department of Medicine, University of Siena (Italy)<\/p>\n Prof Pietro Li\u00f2 \u2013<\/strong> Department of Computer Science and Technology , University of Cambridge, Cambridge, (UK)<\/p>\n Dr Leonardo Rundo<\/strong> \u2013 Department of Radiology, University of Cambridge, Cambridge (UK )<\/p>\n Simeon Spasov<\/strong> \u2013 Department of Computer Science and Technology , University of Cambridge, Cambridge (UK)<\/p>\n Dr Andrea Tangherloni <\/strong>\u2013 Department of Haematology, University of Cambridge, Cambridge, (UK)<\/p>\n Jin Zhu <\/strong>\u2013 Department of Computer Science and Technology , University of Cambridge, Cambridge, UK<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.26.6″ module_id=”submission”][et_pb_column type=”4_4″ _builder_version=”3.26.6″][et_pb_text _builder_version=”3.26.6″]<\/p>\n \n Submissions for the MALCI_MUOMI 2020 workshop will be received through the online system used for the main conference.<\/p>\n 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.<\/p>\nSubmission<\/h3>\n
How to submit a contribution<\/h4>\n
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