This paper describes an experimental computer-aided detection and diagnosis system for breast cancer, the most common form of cancer among women, using mammography. The system relies on the Multiple-Instance Learning (MIL) paradigm, which has proven useful for medical decision support in previous works from our team. In the proposed framework, the breasts are first partitioned adaptively into regions. Then, either textural features, or features derived from the detection of masses and microcalcifications, are extracted from each region. Finally, feature vectors extracted from each region are combined using an MIL algorithm (Citation k-NN or mi-Graph), in order to recognize "normal" mammography examinations or to categorize examinations as "normal", "benign" or "cancer". An accuracy of 91.1% (respectively 62.1%) was achieved for normality recognition (respectively three-class categorization) in a subset of 720 mammograms from the DDSM dataset. The paper also discusses future improvements, that will make the most of the MIL paradigm, in order to improve "benign" versus "cancer" discrimination in particular.