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

该项目将开发一种改进的乳腺癌计算机辅助诊断（CAD）系统，该系统包括乳房x光摄影、超声波和磁共振成像。该系统将基于最近开发的深度学习技术，该技术具有以统一和最佳方式处理多模态数据的能力。该技术的优点在于，它能够自动学习每个模态中需要分析的相关特征以及它们之间隐藏的关系。深度信念网络的使用在语音识别等多个领域产生了令人鼓舞的结果，因此该项目相信我们的方法有潜力提高乳腺癌检测的敏感性和特异性。

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.