Breast cancer is one of the most common malignant tumors in women. In mammogram retrieval system, the query mass
is ambiguity and difficult to be described because in which the lesion and the normal tissue are physically adjacent. If the
query mass can be processed as an image bag, then the ambiguity can be tackled by multi-instance learning (MIL)
techniques. In this paper, we presented a preliminary study of MIL for mass retrieval in digitized mammograms, and
proposed three image bag generators named J-Bag, A-Bag and K-Bag, respectively. Diverse Density (DD), EM-DD and
BP-MIP were applied as MIL algorithms for mass retrieval. Experimental study was carried out on DDSM database and
another database in which images were collected from the Zhejiang Cancer Hospital in China. Preliminary experiments
showed that the MIL techniques can be applied to the problem of mass retrieval in digitized mammograms and the
proposed bag generators A-Bag and K-Bag can achieve more efficient results than the existing bag generator SBN.
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