Maths is Core to Cancer Care: The Melanoma Case, Prof. Pablo Fernandez-Penas (Dermatology) In clinical care, we use numbers to understand most of the biology components of the human being and decide diagnosis and treatments. Measures such as blood pressure or glucose levels have been around for too many years. But there has been areas that have escaped to quantification and analysis, and one of the most critical ones for skin cancer and melanoma in particular is imaging. Dermatology is a visual specialty that relies on the skills of humans to make diagnosis. To add more complexity, if clinicians canât make a diagnosis, the biopsies they take to help them are read by humans using, again, their visual skills. The time is coming for a more objective, quantifiable measurements to help with these visual challenges, and for this information to be combined with other sources of clinical data. AI for Biomedical Image Analysis - Experiences with Skin Lesion Images, Assoc. Prof. Jinman Kim (School of Information Technologies) Medical imaging has an indispensable role in patient management in modern healthcare. There are numerous medical imaging modalities available; they vary in complexity and ’sophistication’ from plain digital chest X-rays to simultaneous functional and anatomical imaging with positron emission tomography (PET) and computed tomography (CT) imaging (PET-CT) using the one device. The challenge is now on how to maximize the extraction of meaningful information from the images while not overloading the user. Fortunately, in parallel to the imaging improvements, we are in an era of artificial intelligence (AI) fuelling the growth of smart decision support and analysis tools for medical image interpretation. In a matter of few years, we have seen rapid rise in research algorithms being integrated to computed aided diagnosis (CAD) systems for clinical use. Yet, from an engineering view, we are only at the infancy of the AI revolution towards healthcare. This talk will present the trend in AI development for medical images, with examples on our research in skin lesion image analysis.