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 and estimates most, if not all, of the components of a deep neural network-based solution for 3D medical image segmentation.

The proposed method can predict the relationship between different training configurations and neural networks, which can be used for comparison of solutions.

He introduces a new search space for neural architectures and a predictor-based AutoML algorithm to accommodate the large search space. Experiments show that the proposed method can achieve state-of-the-art performance on large-scale lesion segmentation datasets compared to other existing methods in the literature. Furthermore, the proposed method has been shown to transfer efficiently to different datasets. The talk also discusses other topics in medical image segmentation, such as transformer-based networks, segmentation in federated learning, segmentation in semi-supervised learning, shape priors in segmentation, etc.

#healthcare, #automl, #medicalimaging

[insidiousmaximus]

you need to invest in some professional speakers to present these videos. They are painful.