Distante received the master in Computer Science at the University Bari, and PhD in Engineering at University of Salento (Italy). His main expertise are in the field of Artificial Intelligence, in particular Computer Vision and Pattern Recognition, Image Processing, Machine Learning and Robotics. Particular emphasis is devoted to the development of Deep Learning algorithms. He has been a visiting researcher at the Computer Science Department of the University of Massachusetts (Amherst, MA) where has carried out research activities under robotics, artificial neural networks and vision. Dr. Distante joined the Faculty of the University of Massachusetts as a Teaching Assistant, for the “Artificial Intelligence” class of the Master in Sciences 1998. He joined in 2001 the Italian National Research Council of Italy CNR. Since 2003 he is Contract Professor for Computer Vision, Pattern Recognition and Image Processing in Computer Engineering at the University of the Salento IT. He founded the Taggalo CNR’s spinoff company. In 2011, Dr. Distante has been awarded with the national innovation Prize working capital PNI-Cube TelecomItalia with the Taggalo project. He won the ChallengUP (Cisco, Intel and Deutch Telekom acceleration program) contest in 2015 with Taggalo. He served as a general chair of ACIVS2016, IEEE Advanced Video and Signal based surveillance AVSS 2017 AVSS2021, VAAM workshop Video analytics for Audience measurements and DeepRetail. He is in the programm committee of CVPR, ECCV, ICCV, AVSS, Visual, IAV, SPIE Multimodal Sensing and AI, Big Knowledge, ISCV. He is coordinating two joint research labs with industries in security and healthcare sectors. He is the unit manager of the Institute of Applied Sciences and Intelligent System of the CNR. He is expert member in innovation for the Ministry of Education, Research and University (MIUR), as well as the Ministry of economic development (MISE) and a few regional agencies for technology and innovation.
Deep learning for computer vision in medicine
We are in the AI era, where deep learning (DL) has reinvigorated the scientific field by demonstrating the potential for many fields including medicine. DL has been widely used in various medical imaging tasks with noticeable success in many applications. In this talk, I will show how DL can be used to help doctors during the decision process while evaluating medical imaging data. Applications such as dermatology, digital pathology and radiomics will be covered. However, in the medical sector the AI revolution is yet to come, since it is known that the success of AI is mostly attributed to the availability of annotated data. This aspect along with privacy issues are key technical challenges, due to the small amount of available data, difficulties to enroll experts in the time consuming annotation process along with the data disclosure. To this aim, I will discuss on emerging trends and hurdles required for real-world clinical deployment.