False Positive Reduction in Lung Computed Tomography Images using Convolutional Neural Networks

Recent studies have shown that lung cancer screening using annual low-dose computed tomography (CT) reduces lung cancer mortality by 20% compared to traditional chest radiography. Therefore, CT lung screening has started to be used widely all across the world. However, analyzing these images is a serious burden for radiologists. In this study, we propose a novel and simple framework that analyzes CT lung screenings using convolutional neural networks (CNNs) and reduces false positives. Our framework shows that even non-complex architectures are very powerful to classify 3D nodule data when compared to traditional methods. We also use different fusions in order to show their power and effect on the overall score. 3D CNNs are preferred over 2D CNNs because data are in 3D, and 2D convolutional operations may result in information loss. Mini-batch is used in order to overcome class-imbalance. Proposed framework has been validated according to the LUNA16 challenge evaluation and got score of 0.786, which is the average sensitivity values at seven predefined false positive (FP) points.Comment: 4 page

Искусственный интеллект в скрининге рака легкого: оценка диагностической точности алгоритма для анализа низкодозовых компьютерных томографий

The diagnostic accuracy of the artificial intelligence algorithm aimed to detect lesions on low-dose computer tomograms has been independently assessed. The dataset formed as part of the lung cancer screening program in Moscow was used. The following indicators have been defined: sensitivity – 0.817%, specificity – 0.925%, accuracy – 0.860%, area under the characteristic curve – 0.930. High accuracy rates demonstrated through the independent assessment indicate a good reproducibility of the results by artificial intelligence using independent data about the population of MoscowПроведена независимая оценка диагностической точности алгоритма искусственного интеллекта для выявления очагов поражения на низкодозовых компьютерных томограммах. Использован датасет, сформированный в рамках программы скрининга рака легкого в г. Москве. Определены следующие показатели: чувствительность – 0,817%, специфичность – 0,925%, точность – 0,860%, площадь под характеристической кривой – 0,930. Высокие показатели точности, полученные при независимом тестировании, свидетельствуют о хорошей воспроизводимости результатов работы искусственного интеллекта на независимых данных, относящихся к популяции г. Москвы