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京东 11.11 红包
[#22-18] MICS在线学术讲座：杨栋（Modern Medical Image Segmentation, AutoML, and Bes
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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 are mainly applied on 2D network based applications (due to the computational budget), or focus on individual components of deep learning solutions (e.g., neural architecture, hyper-parameters), which could lead to sub-optimal results. In this talk, I will take a systematic perspective, and introduce novel algorithms which automatically considers and customized most, if not all, of the components of a deep neural network-based solution for 3D medical image segmentation. The proposed algorithm can not only optimize the neural network architecture, but also estimate the hyper-parameters or other components of the model training recipes. Specifically, I will introduce new neural architecture search spaces and new AutoML algorithms to accommodate large search spaces. Experiments show that the proposed algorithms can achieve state-of-the-art performance on large-scale 3D medical image segmentation datasets.
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