Robust, valid and interpretable deep learning for quantitative imaging

使用人工智能的最大挑战之一是所使用模型的“黑箱”性质。本项目旨在提高定量磁共振成像中深度学习的有效性和可信度。深度学习在加速复杂的图像处理任务方面有很大的希望，但目前受到可变数据输入的影响，预测不能保证是可信的，最终用户也不清楚结果的可靠性。这些成果旨在提供人工智能和机器学习方面的先进知识和能力，澳大利亚迫切需要利用这些知识和能力，将深度学习带入实际应用，从而产生经济、商业和社会影响。

One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it is not clear to the end user how reliable the results are. The outcomes intend to deliver advanced knowledge and capability in artificial intelligence and machine learning that Australia urgently needs to capitalise on bringing deep learning into practical applications delivering economic, commercial and social impact.