Automated analysis of multi-modal medical data using deep belief networks

使用深度信念网络自动分析多模式医疗数据

• 批准号: DP140102794
• 负责人: Prof Gustavo Carneiro
• 金额: $ 19.91万
• 依托单位: The University of Adelaide
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2014
• 资助国家: 澳大利亚
• 起止时间: 2014-12-30 至 2018-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP140102794
• 关键词: Automated; analysis; multi; modal; medical

This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features to analyse in each modality and the hidden relationships between them. The use of deep belief networks has produced promising results in several fields, such as speech recognition, and so this project believes that our approach has the potential to improve both the sensitivity and specificity of breast cancer detection.

该项目将开发一种改进的乳腺癌计算机辅助诊断（CAD）系统，该系统将乳腺X线摄影、超声和磁共振成像结合起来。该系统将基于最近开发的深度学习技术，该技术能够以统一和最佳的方式处理多模态数据。这种技术的优点是，它能够自动学习每种模态中要分析的相关特征以及它们之间的隐藏关系。深度信念网络的使用已经在语音识别等多个领域产生了有希望的结果，因此该项目认为我们的方法有潜力提高乳腺癌检测的灵敏度和特异性。