Adapting Deep Learning for Real-world Medical Image Datasets

该项目旨在研究新的深度学习建模方法，以利用包含噪声和不完整标签以及不平衡的类别流行度的真实世界大规模图像数据集--以使这些数据集能够用于对深度学习分类器进行建模。预期成果包括建立深度学习分类器模型的创新方法。这项研究将涉及与机器学习和医学图像分析研究机构的新的跨学科和国际合作。这应该会带来显著的好处，例如更好地理解深度学习理论，可以使用以前未探索的数据集的新的深度学习应用程序，以及对未来澳大利亚劳动力的培训。

The project aims to investigate new deep learning modelling approaches to leverage real-world large-scale image data sets that contain noisy and incomplete labels and imbalanced class prevalence – to enable the use of these data sets for modelling deep learning classifiers. Expected outcomes include an innovative method for modelling deep learning classifiers. The research will involve new inter-disciplinary and international collaborations with machine learning and medical image analysis research institutions. This should provide significant benefits, such as better understanding of deep learning theory, new deep learning applications that can use previously unexplored data sets, and training for the future Australian workforce.