Objective Diseases characteristic for modern western civilization, such as cancer, diabetes or cardiovascular disorders, lead to millions of deaths per year in the European Union. In order to decrease this enormous quantity, medical imaging should be widely available at early diagnostics and every stage of a therapy. Nowadays, there are various diagnostics techniques including CT, PET, MRI, however, analysis of a medical image is time-consuming and expensive. Development of new effective automatic tool for medical imaging will appear a new strategy in highly specific control of incidences and disease progression. The aim of the DeeBMED project is to develop powerful automatic medical imaging tool that can cope with main problems associated with complex images like medical scans: multimodality of data distribution, large number of dimension and small number of examples, small amount of labeled data, multi-source learning, and robustness to transformations. In this project I will propose a probabilistic framework that combines different deep neural networks (DNN), such as feedforward nets, convolutional nets, Gaussian processes. I will apply DNN to model probabilistic relationships among a medical scan, a disease label, and hidden variables representing latent factors in data. In the case of a small sample size DNN are prone to overfitting. A possible remedy for that is Bayesian learning, however, it is still challenging how to apply it to DNN. In this project I will use various approaches: modelling weights uncertainty, Dropout, Bayesian Distillation. As the result I predict identification of the first highly effective medical imaging analysis tool that in the future will be widely used by radiologists in medical institutes in the whole EU. Novel automation will drastically reduce time and costs of analysis and provide more accessible diagnostics. The project will be carried out at the University of Amsterdam, under the supervision of Prof. Max Welling. Fields of science natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learningengineering and technologymedical engineeringdiagnostic imaging Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2015-EF - Marie Skłodowska-Curie Individual Fellowships (IF-EF) Call for proposal H2020-MSCA-IF-2015 See other projects for this call Funding Scheme MSCA-IF-EF-ST - Standard EF Coordinator UNIVERSITEIT VAN AMSTERDAM Net EU contribution € 177 598,80 Address Spui 21 1012WX Amsterdam Netherlands See on map Region West-Nederland Noord-Holland Groot-Amsterdam Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
