Half day Tutorials

Target Class Learning for Anomaly/Outlier Detection: a robust strategy
Instructors: P Nagabhushan, Sonali Agarwal, Sanjay Sonbhadra, Narinder Singh Punn
Machine learning techniques have advanced exponentially in recent years. These improved technologies are adopted in several application domains. An anomaly is an abnormal pattern that exists in the data and in all real-time applications, anomaly detection is the most crucial task. The anomaly or outlier detection task becomes more challenging when only the target class (class of interest) samples are available during training and other class samples are either ill-defined or absent. In this context, several solutions have been offered, but despite the extensive technological developments, anomaly/novelty detection is still a challenging task and there is enough scope to mimic the learning behaviour of the human brain. Following the capability of the brain to simultaneously analyze the anomaly, one-class classification strategies are adopted for better learning of the target class. Apart from the huge sample space, the high dimension of the data adds computational overhead along with its intrinsic property of curse of dimensionality. The learning models exhibit better performance in the presence of the most promising training samples and discriminating features. The selection of training samples and features must be supervised by only the target class samples to ensure strong separation from outliers. With this motivation, this tutorial presents the recent advancements in target class learning for anomaly detection while covering fundamentals, use-cases, applications, and challenges. The tutorial also discusses future research possibilities and necessary challenges. See more ....

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 ....