Ms. Luyan Yao Profile

Luyan Yao

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Proceedings Article | 3 April 2023 Presentation + Paper

Full-field digital mammogram retrieval using Fourier feature-based auto-encoder: application in breast cancer screening training

Luyan Yao, Jiefei Wei, Xin Chen, Yan Chen

Proceedings Volume 12467, 124670K (2023) https://doi.org/10.1117/12.2653854

KEYWORDS: Image retrieval, Feature extraction, Education and training, Breast, Machine learning, Medical imaging, Artificial intelligence, Breast cancer, Deep learning, Digital mammography

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Using manually selected cases for breast screener training is time-consuming and prohibitively costly. However, retrieving medical images of breasts via computer vision methods that reflect diagnostically relevant visual features is challenging because the overall appearance variability of whole breasts is high compared to often subtle lesions of interest. Our work aims to develop an automatic and low-costing tool for retrieving and recommending similar full-field digital mammograms (FFDMs) of breast cancer by providing a query image. This tool will help to identify poor reader performance in real-life screening that allows interventions to change practice according to error cases, such as reviewing practice or further training. The core element of this tool is automatic content-based image retrieval. In this paper, we propose an unsupervised method for training an artificial intelligence (AI) model to rank FFDMs according to their similarity in visual features. The proposed method consists of a patch-based pre-processing step, an auto-encoder with Fourier feature mapping and a K-nearest neighbour (K-NN)-based similarity ranking method. To evaluate our method, we used 2500 images (30% abnormal images) from the regular assessment database of UK radiologists over the last ten years. We compared our method with a numerical-data-based method. The results have shown the advantages of our proposed method in retrieving similar cases by considering much richer image information (including but not limited to the breast density, lesion size and location, etc.) without any labour-intensive data labelling.

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