Robust, valid and interpretable deep learning for quantitative imaging

用于定量成像的稳健、有效且可解释的深度学习

• 批准号: LP200301393
• 负责人: Dr Steffen Bollmann
• 金额: $ 27.34万
• 依托单位: The University of Queensland
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-02-15 至 2025-02-14
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP200301393
• 关键词: Robust; valid; interpretable; deep; learning

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.

采用人工智能面临的最大挑战之一是所用模型的“黑箱”性质。该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，但目前存在数据输入可变、预测不一定可信以及最终用户不清楚结果有多可靠的问题。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，产生经济、商业和社会影响。