An AI-based method to retrieve hematoxylin and eosin breast histology images using mammograms

Authors Organisations
Type Conference Proceeding (Non-Journal item)
Original languageEnglish
Title of host publication15th International Workshop on Breast Imaging, IWBI 2020
EditorsHilde Bosmans, Nicholas Marshall, Chantal Van Ongeval
PublisherSPIE
ISBN (Electronic)9781510638310
DOI
Publication statusPublished - 22 May 2020
Event15th International Workshop on Breast Imaging, IWBI 2020 - Leuven, Belgium
Duration: 25 May 202027 May 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11513
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference15th International Workshop on Breast Imaging, IWBI 2020
CountryBelgium
CityLeuven
Period25 May 202027 May 2020
Links
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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.

Keywords

  • Deep Learning, Hematoxylin &Eosin (H&E), Histology, Image Retrieval, Mammography