The introduction of Full-Field Digital Mammography (FFDM) in breast screening has brought with it several advantages in terms and processing facilities and image quality and Computer Aided Detection (CAD) systems are now sprouting that make use of this modality. A major drawback however, is that FFDM data is still relatively scarce and therefore, CAD system's performance are inhibited by a lack of training examples. In this paper, we explore the incorporation of more ubiquitous Screen Film Mammograms (SFM) and FFDM processed by the manufacturer, in training a system for the detection of tumour masses. We compute a small set of additional quantitative features in the raw data, that make explicit use of the log-linearity of the energy imparted on the detector in raw FFDM. We explore four di erent fusion methods: a weighted average, a majority vote, a convex combination of classi er outputs, based on the training error and an additional classi er, that combines the output of the three individual label estimates. Results are evaluated based on the Partial Area Under the Curve (PAUC) around a clinically relevant operating point. All fusion methods perform signi cantly better than any of the individual classi ers but we nd no signi cant di erence between the fusion techniques.