Deep neural networks (DNNs) have been successfully applied to volume segmentation and other medical imaging tasks. They are capable of achieving state-of-the-art accuracy and can augment the medical imaging workflow with AI-powered insights.
However, training robust AI models for medical image analysis is time-consuming and tedious and requires iterative experimentation with parameter tuning. On the other hand, automated machine learning (AutoML) has been studied and developed for years in academia and industry. Its objective is to construct AI models without the need for human heuristics.
This tutorial of the most recent advancement of AutoML aims to enable researchers and data scientists with cutting-edge tools for AI development in medical image analysis.
The AutoML functionality makes the process of neural architecture search and hyper-parameter tuning seamless by intelligently searching for the optimal parameter settings to train models automatically. The underlying algorithms give data scientists a configurable environment to define the training workflow experiments. Moreover, it provides a standard way to implement state-of-the-art deep learning (DL) solutions.
Topic | Speaker | ||
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Introduction | TBD | TBD | TBD |
Basics and Fundamentals | |||
Automated Machine Learning and Neural Architecture Search | TBD | TBD | TBD |
Performance Tuning Guide | |||
Performance Tuning Guide via AutoML | TBD | TBD | TBD |
Application Case Studies | |||
Application #1 | TBD | TBD | TBD |
Application #2 | TBD | TBD | TBD |
Application #3 | TBD | TBD | TBD |
Dongnan Liu | Daguang Xu | Dong Yang | Yefeng Zheng | Zhuotun Zhu |