Modern Medical Image Segmentation, AutoML, and Beyond

Nowadays, with technological advancements in algorithm design (such as deep learning) and hardware platforms (such as GPUs), medical image analysis has become a critical step in disease understanding, clinical diagnosis, and treatment planning. Among various tasks, image segmentation has been one of the most important medical image analysis tasks. Recently, deep convolutional neural networks have been widely applied in medical image segmentation with state-of-the-art performance. Meanwhile, Automated Machine Learning (AutoML) has also been explored in deep neural networks, aiming to further enhance model efficiency and effectiveness. However, the existing AutoML algorithms have taken on a singular perspective and focused on separate components of deep learning solutions (e.g., neural architecture, hyperparameters), which could lead to suboptimal results. In this talk, Dong Yang, Applied Research Scientist at NVIDIA, takes a systematic perspective, and introduce a novel method which automatically considers a