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22 May 2020 An AI-based method to retrieve hematoxylin and eosin breast histology images using mammograms
Azam Hamidinekoo, Erika Denton, Kate Honnor, Reyer Zwiggelaar
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Proceedings Volume 11513, 15th International Workshop on Breast Imaging (IWBI2020); 1151319 (2020) https://doi.org/10.1117/12.2564298
Event: Fifteenth International Workshop on Breast Imaging, 2020, Leuven, Belgium
Abstract
Early diagnosis of breast cancer can increase survival rate. The assessment process for breast screening follows a triple assessment model: appropriate imaging, clinical assessment and biopsy. Retrieving prior cases with similar cancer symptoms could be used to circumvent incompatibilities in breast cancer grading. Abnormal mass lesions in breast are often co-located with normal tissue, which makes it difficult to describe the whole image with a single binary code. Therefore, we propose an AI-based method to describe mass lesions in semantic abstracts/codes. These codes are used in a searching based method to retrieve similar cases in the archive. This simple and effective network is used for unifying classification and retrieval in a single learning process, while enforcing similar lesion types to have similar semantic codes in a compact form. An advantage of this approach is its scalability to large-scale image retrievals.
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Azam Hamidinekoo, Erika Denton, Kate Honnor, and Reyer Zwiggelaar "An AI-based method to retrieve hematoxylin and eosin breast histology images using mammograms", Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 1151319 (22 May 2020); https://doi.org/10.1117/12.2564298
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KEYWORDS
Mammography
Image retrieval
Breast
Data modeling
Biopsy
Classification systems
Feature extraction
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