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What is this project about?

This project aims to develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates text, mammography (MG), ultrasound (US) and magnetic resonance imaging (MRI), using deep belief networks (DBN). This system optimally integrates information from any combination of these data sources, so as to improve the efficiency, sensitivity and specificity of breast cancer diagnosis.


This CAD system, using multi-modal data and deep belief networks techniques, has the aims at:

  • Automatically detect and segment suspicious regions from different breast imaging data, e.g., MG, US and MRI.

  • Estimate BI-RADS scores (from 0 to 6) from the segmentations and the patient’s clinical records.

  • Retrieve similar cases from our database, given the above estimation results.

  • Automatically extract relevant features from any combination of input data types.

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