“Biomedical Image Segmentation using Deep Learning”

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.