Mr. Andrei Krendal Profile

Andrei Krendal

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Proceedings Article | 30 September 2024 Poster + Paper

Breast abnormalities segmentation using neural networks for feature extraction followed by unsupervised anomaly detection

Vladislav Pryadka, Andrei Krendal, Vitaly Kober

Proceedings Volume 13137, 131371F (2024) https://doi.org/10.1117/12.3028085

KEYWORDS: Pathology, Image segmentation, Education and training, Mammography, Cancer detection, Feature extraction, Neural networks, Tumors, Computing systems

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This study focuses on the problem of finding pathologies on mammogram screenings using computer vision-based systems. For that we use deep neural networks and use open-source datasets for our models training for finding segments of pathologies on screenings. These datasets are also used for further analyses of different supervised training techniques, functions and losses to achieve best results for generalization of data even when its’ domain changes. We don’t use screenings with pathologies during training while trying to achieve the results where our network would be able to still recognize these pathologies and embeddings (feature maps) extracted at certain layers of our network would allow to group up the samples where these pathologies are present by calculating either cosine or normalized L2 distances between samples. First, we train our network to recognize normal mammogram screenings and anything that is different. We create a vocabulary of known classes using extracted embeddings of samples from training subset to obtain centroids of embeddings for 2 separate classes – benign and malignant tumors. We then use a method known as CAM (Class Activation Mapping) along with Selective Search algorithm for segmentation task. In our work we use mainly dataset CBIS-DDSM [1] for validation which consists of a few hundreds of annotated images of digitalized mammogram screenings. The entire pipeline of the system has the following steps: first, extracting the embeddings from training subset and clustering them into vocabularies for classes of benign and malignant tumors; then, the clusters can be analyzed and regrouped manually depending on the situation to create sub-classes such as small or big benign tumors; afterwards, we use these existing vocabularies for classification task to detect screenings with tumors; and to obtain segments of the detected pathologies CAM along with Selective Search with carefully selected parameters is used.

Proceedings Article | 4 October 2023 Poster + Paper

Breast abnormalities classification using deep learning feature extraction

Vladislav Pryadka, Andrei Krendal, Jose Gonzalez-Fraga, Vitaly Kober, Anastasia Kober

Proceedings Volume 12674, 126741M (2023) https://doi.org/10.1117/12.2677167

KEYWORDS: Pathology, Mammography, Feature extraction, Image segmentation, Deep learning, Education and training, Breast, Classification systems, Cancer detection, Breast cancer

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In this study, the main goal is to improve the performance of existing computer diagnostic systems by proposing new processing methods. We use the public CBIS-DDSM dataset for training and validation. The dataset consists of normal screenings with benign tumors and malignant tumors, with all pathologies carefully selected and checked by a radiologist. The data set also includes ROI masks and pathology bounding boxes, as well as labels corresponding to the class of each pathology diagnosis. To achieve better results on the dataset, we transform the data for their more efficient representation using autoencoders in order to obtain features with low intraclass and high interclass variance, and apply LDA to the encoded features to classify pathologies. Methods for automated pathology detection are not considered in this article, since it is mainly focused on the classification task itself. The entire pipeline of the system consists of the following steps: first, feature extraction using pathology segmentation; dividing the data into two clusters; feature transformation using linear discriminant analysis to minimize intra-class variance; finally, the classification of pathologies. The results of this study for the classification of pathologies using various deep learning methods are presented and discussed.

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