Nowadays, the amount of heterogeneous biomedical data is increasing more and more thanks to the advances in imaging acquisition modalities and high-throughput technologies. This huge information ensemble could overwhelm the analytic capabilities needed by physicians in their daily decision-making tasks as well as by biologists investigating complex biochemical systems. Quantitative imaging methods convey scientifically and clinically relevant information in prediction, prognosis or treatment response assessment, by also considering radiomics approaches. Therefore, the computational analysis of medical and biological images plays a key role in radiology and laboratory applications. In this regard, frameworks based on advanced Machine Learning and Computational Intelligence can significantly improve traditional Image Processing and Pattern Recognition approaches. However, conventional Artificial Intelligence techniques must be adapted and tailored to address the unique challenges concerning biomedical imaging data.
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 novel 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.