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Proceedings Paper

Multi-instance learning for mass retrieval in digitized mammograms
Author(s): Pengfei Lu; Wei Liu; Weidong Xu; Lihua Li; Bin Zheng; Juan Zhang; Lingnan Zhang
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Paper Abstract

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.

Paper Details

Date Published: 23 February 2012
PDF: 8 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831523 (23 February 2012); doi: 10.1117/12.911675
Show Author Affiliations
Pengfei Lu, Hangzhou Dianzi Univ. (China)
Wei Liu, Hangzhou Dianzi Univ. (China)
Weidong Xu, Hangzhou Dianzi Univ. (China)
Lihua Li, Hangzhou Dianzi Univ. (China)
Bin Zheng, Hangzhou Dianzi Univ. (China)
Univ. of Pittsburgh Medical Ctr. (United States)
Juan Zhang, Zhejiang Cancer Hospital (China)
Lingnan Zhang, Zhejiang Cancer Hospital (China)

Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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