Breast mass characterization using sparse approximations of patch-sampled deep features

Chelsea E. Harris; Sokratis Makrogiannis

doi:10.1117/12.2653926

7 April 2023 Breast mass characterization using sparse approximations of patch-sampled deep features

Chelsea E. Harris, Sokratis Makrogiannis

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124652A (2023) https://doi.org/10.1117/12.2653926
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Diagnosis of breast cancer is often achieved through expert radiologist examination of medical images such as mammograms. Computer-aided diagnosis (CADx) methods can be useful tools in the medical field with applications such as aiding radiologists in making diagnosis decisions. However, such CADx systems require a sufficient amount of data to train on, in conjunction with efficient machine learning techniques. Our Spatially Localized Ensembles Sparse Analysis using Deep Features (DF-SLESA) machine learning model uses local information of features extracted from deep neural networks to learn and classify breast imaging patterns based on sparse approximations. We have also developed a new technique of patch sampling for learning sparse approximations and making classification decisions that we denote as PatchSample decomposition. The PatchSample method differs from our previous approach, our BlockBoost method, in that larger dictionaries are constructed that hold not just spatial-specific information, but a larger collective of visual information from all locations in the region of interest (ROI). Of note is that we trained and tested our method on a merged dataset of mammograms obtained from two sources. Experimental results have reached up to 67.80% classification accuracy (ACC) and 73.21% area under the ROC curve (AUC) using PatchSample decomposition on a merged dataset consisting of the MLO view regions of interest of the MIAS and CBIS-DDSM datasets.

Citation Download Citation

Chelsea E. Harris and Sokratis Makrogiannis "Breast mass characterization using sparse approximations of patch-sampled deep features", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124652A (7 April 2023); https://doi.org/10.1117/12.2653926

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