Half day Tutorials

AI techniques to combat COVID-19
Instructors: Sonali Agarwal, Narinder Singh Punn, Sanjay Sonbhadra
The rampant outbreak of the novel coronavirus (COVID-19, SARS-Cov-2), during early December 2019 in Wuhan, China, has created a staggering worldwide crisis along with the widespread loss of lives. The scarcity of resources and lack of experiences to endure the COVID-19 pandemic, combined with the fear of future consequences has established the need for adoption of Artificial Intelligence (AI) techniques to address the challenges. Motivated by the need to highlight the need for employing AI in combating the COVID-19 pandemic, this tutorial aims to help the audience to gain comprehensive understanding of the current state of AI applications in developing the computer-assisted (controlling, monitoring, discovery, diagnosing and treatment) systems to battle the COVID-19 crisis along with the AI assisted spread containment measures. See more ....

A tutorial on biomedical image segmentation using deep learning
Instructors: Sonali Agarwal, Krishna Pratap Singh, Narinder Singh Punn, Sanjay Sonbhadra
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. Most of the medical applications require identifying and localizing the objects or regions (damaged tissues, cells or nuclei) found in the medical imaging such as CAT scans, X-Rays, Ultrasound, etc. for diagnosis, monitoring and treatment. This delineation is generally performed by expert clinicians or radiologists which is a complex and time-consuming task. In recent studies, the implication of transfer learning and U-Net based approaches have illustrated state-of-the-art performance in different applications for the development of computer-aided diagnosis systems to localize the infected or damaged tissues or cells in the body using various modalities for early diagnosis and treatment of diseases such as brain tumor, lung cancer, alzheimer, breast cancer, etc. With this motivation, this tutorial focuses on the state-of-the-art in Transfer and Deep Learning, a critical discussion of open challenges and directions for future research in the area of biomedical image segmentation. See more ....