Challenges of R&D commercialization in Russia

This article aims at stating the significance of research and development commercialization in Russia, as well as changes having taken place in this area over the last decade. It also covers major challenges both research and development buyers and sellers face, and namely the absence of linking element between researchers/developers and companies, interaction between Russian science, development and industrial enterprises under the new market conditions, risk of failure, and underdeveloped innovation infrastructure.Эта статья посвящена значимости коммерциализации научных разработок в России, изменениям, произошедшим в этой сфере за последнее десятилетие. Рассматриваются основные проблемы, с которыми сталкиваются как покупатели, так и продавцы научных разработок, а именно: отсутствие связующего звена между учеными и компаниями, взаимодействие российских научно-технических и промышленных предприятий в новых рыночных условиях, риск неудачи, неразвитость инновационной инфраструктуры

Challenges of R&D commercialization in Russia

This article aims at stating the significance of research and development commercialization in Russia, as well as changes having taken place in this area over the last decade. It also covers major challenges both research and development buyers and sellers face, and namely the absence of linking element between researchers/developers and companies, interaction between Russian science, development and industrial enterprises under the new market conditions, risk of failure, and underdeveloped innovation infrastructure.Эта статья посвящена значимости коммерциализации научных разработок в России, изменениям, произошедшим в этой сфере за последнее десятилетие. Рассматриваются основные проблемы, с которыми сталкиваются как покупатели, так и продавцы научных разработок, а именно: отсутствие связующего звена между учеными и компаниями, взаимодействие российских научно-технических и промышленных предприятий в новых рыночных условиях, риск неудачи, неразвитость инновационной инфраструктуры

Formation problems of economy in Russia

This article examines various roadblocks rising in the way of innovative economy formation in modern Russia. It also suggests potential integrated solutions to these problems.В статье рассмотрены различные препятствия на пути становления инновационной экономики в России на современном этапе. Предложены возможные пути комплексного преодоления данных проблем

Contour-aware multi-label chest X-ray organ segmentation

© 2020, CARS. Purpose: Segmentation of organs from chest X-ray images is an essential task for an accurate and reliable diagnosis of lung diseases and chest organ morphometry. In this study, we investigated the benefits of augmenting state-of-the-art deep convolutional neural networks (CNNs) for image segmentation with organ contour information and evaluated the performance of such augmentation on segmentation of lung fields, heart, and clavicles from chest X-ray images. Methods: Three state-of-the-art CNNs were augmented, namely the UNet and LinkNet architecture with the ResNeXt feature extraction backbone, and the Tiramisu architecture with the DenseNet. All CNN architectures were trained on ground-truth segmentation masks and additionally on the corresponding contours. The contribution of such contour-based augmentation was evaluated against the contour-free architectures, and 20 existing algorithms for lung field segmentation. Results: The proposed contour-aware segmentation improved the segmentation performance, and when compared against existing algorithms on the same publicly available database of 247 chest X-ray images, the UNet architecture with the ResNeXt50 encoder combined with the contour-aware approach resulted in the best overall segmentation performance, achieving a Jaccard overlap coefficient of 0.971, 0.933, and 0.903 for the lung fields, heart, and clavicles, respectively. Conclusion: In this study, we proposed to augment CNN architectures for CXR segmentation with organ contour information and were able to significantly improve segmentation accuracy and outperform all existing solution using a public chest X-ray database