Publications

Extracting clinical information from chest x-ray reports: A case study for Russian language

Published in 2020 NIR Innopolis, 2020

—In this paper, we analyze possible approaches for diagnosis identification in Russian medical reports. Firstly, we introduce the main problems of raw Russian medical reports preprocessing. Secondly, focusing on the embedding extraction method, we analyzed several publicly available models and discovered that the use of BERT model is a promising instrument for this task. Performing the first attempt to build the NLP system for the Russian medical report classification based on the embeddings extraction method, we formulated the main weaknesses that limit the use of the existing publicly available Russian NLP models in the medical-text domain. Having no labeled data available, we evaluate each model visually, analyzing embeddings representation in 2D field retrieved by dimensionality reduction using t-SNE. We assume that a good model will be able to place reports that describe the same diagnosis close to each other, while moving reports with distinct diagnoses far from each other, forming clusters. Finally, we proposed several ways of possible future research that, as we believe, will improve the results achieved in this field so far.

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AUTOMATING CARDIOMETRIC MEASUREMENTS IN CHEST X-RAYS

Published in to be published on ISBI 2021, 2020

The analysis of the positions, shapes, and sizes of thoracicorgans is an internationally established practice for radiolo-gists. The considerable amount of time spent on manual mea-surements of roentgenographic features reveals the need fora computerized approach for the automation of these mea-surements. In this work, we introduce a new way for theannotation of the chest x-ray data in thoracic ratios estima-tion. Using a manually annotated dataset, we developed adeep learning-based approach to infer three cardiometrics inchest radiographs, namely the Cardiothoracic Ratio, Lupi Co-efficient, and Moore Coefficient. The cardiometrics of inter-est are defined as ratios of line segments drawn over chestX-rays. We encoded the line segments with landmarks andapplied an hourglass model for landmark detection. To thebest of our knowledge, this is the first study aiming to estimatetwo out of three aforementioned cardiometrics. We comparedthe performance of the proposed solution with intraobservervariability of a radiologist using the test-retest strategy with aone-year break. We found out that human performance is notequally consistent across different measurements with morethan 20% difference in the F1-score metric

Automated Localization of Lung Nodules from Chest X-rays With Deep Neural Networks

Published in 2020 ASTRO Annual Meeting, 2020

Lung cancer constitutes more than 20% of all cancer deaths in the Russian Federation. About 34.2% of these cases were diagnosed late, which significantly reduces the life expectancy of patients. Chest X-rays are the main screening method for lung cancer in Russia. The small size of nodules and difficult localizations are the reasons why nodules are often missed during routine scanning. The aim of this study is to employ modern deep learning tools for the localization of nodules in chest X-Rays.

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Adopting Confident Learning to Eliminate Uncertainty in Chest X-ray Images for Lung Nodules Prediction

Published in 2020 ASTRO Annual Meeting, 2020

The potential of deep learning to advance lung nodule detection from chest X-rays is significantly compromised by the lack of large annotated databases and noisy labels in the existing databases. The aim of this study is to investigate the applicability of the novel Confident Learning approach for chest X-ray database cleaning and nodule detection improving.

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