Abstract

Nowadays, the amount of heterogeneous biomedical data is increasing more and more thanks to the advancements in imaging acquisition modalities and High-Throughput technologies. This huge information ensemble could overwhelm analytic capabilities concerning both physicians in their decision-making tasks and biologists in investigating complex biological systems. Quantitative imaging methods provide scientifically and clinically relevant data in prediction, prognostication or response assessment, by also exploiting radiomics approaches. In this regard, Machine Learning and Computational Intelligence can significantly improve traditional Image Processing techniques. Therefore, computational approaches for medical and biological image analysis play a key role in radiology and laboratory applications. However, conventional Machine Learning and Computational techniques must be adapted and tailored to address the unique challenges regarding biomedical images. In this talk, the challenges and the characteristics of the most recent methods will be introduced and discussed. I will start with some practical applications exploiting classic Image Processing and Pattern Recognition techniques. Afterwards, a recent medical image enhancement method based on Genetic Algorithms will be briefly described. To conclude, the generalization capabilities of Convolutional Neural Networks in medical image segmentation tasks as well as the generation of realistic medical images based on Generative Adversarial Networks will be investigated.

Short bio: Leonardo Rundo received his Bachelor’s and Master’s Degrees in Computer Science Engineering from the University of Palermo, Italy, in 2010 and 2013, respectively. Since December 2013, he has been Research Fellow at the Institute of Molecular Bioimaging and Physiology, National Research Council of Italy (IBFM-CNR), Cefalù (PA), Italy. He is currently a Ph.D. Student in Computer Science, under the supervision of Prof. G. Mauri, at the University of Milano-Bicocca, Italy. His main scientific research interests include Digital Image Processing, Biomedical Image Analysis, Machine Learning, Computational Intelligence, and High-Performance Computing.