The application of AI to single cell image analysis: examples in health monitoring, disease diagnosis and treatment
In this talk I will discuss the use of machine and deep learning in single cell analysis and demonstrate its use in a wide range of imaging modalities from flow cytometry to 3D confocal microscopy. Several case studies will be discussed ranging from the detection of minimal residual disease in children with Acute Lymphoblastic Leukaemia and the classification of the deterioration of red blood cells for transfusion through to the development of label free techniques in microscopy for the minimisation of biomarkers in imaging experiments. I will focus on the use of the latest deep learning algorithms to score the in-vitro micronucleus assay which is a globally used technique for the quantification of DNA damage required for regulatory compound safety testing in addition to inter-individual monitoring of the environment, lifestyle, and occupational factors.
Paul Rees received his degree and Ph.D. in theoretical physics from Cardiff University and subsequently became a Research Fellow at the Department of Physics, Trinity College, Dublin, Ireland, where his research included the theory of many-body effects in laser diodes. In 1997 he was appointed to a Lectureship in the School of Informatics at Bangor University and moved to Swansea University in 2005 to a Chair in Nanotechnology. During this time, he was instrumental in setting up the Department of Biomedical Engineering where is he now Professor. He is also a Senior Affiliate at the Texas Methodist Hospital Research Institute, Houston and a visiting Professor at the Broad Institute of MIT and Harvard, Boston.