TernausNetV2: Fully Convolutional Network for Instance Segmentation

The most common approaches to instance segmentation are complex and use two-stage networks with object proposals, conditional random-fields, template matching or recurrent neural networks. In this work we present TernausNetV2 - a simple fully convolutional network that allows extracting objects from a high-resolution satellite imagery on an instance level. The network has popular encoder-decoder type of architecture with skip connections but has a few essential modifications that allows using for semantic as well as for instance segmentation tasks. This approach is universal and allows to extend any network that has been successfully applied for semantic segmentation to perform instance segmentation task. In addition, we generalize network encoder that was pre-trained for RGB images to use additional input channels. It makes possible to use transfer learning from visual to a wider spectral range. For DeepGlobe-CVPR 2018 building detection sub-challenge, based on public leaderboard score, our approach shows superior performance in comparison to other methods. The source code corresponding pre-trained weights are publicly available at https://github.com/ternaus/TernausNetV

Feature Pyramid Network for Multi-Class Land Segmentation

Semantic segmentation is in-demand in satellite imagery processing. Because of the complex environment, automatic categorization and segmentation of land cover is a challenging problem. Solving it can help to overcome many obstacles in urban planning, environmental engineering or natural landscape monitoring. In this paper, we propose an approach for automatic multi-class land segmentation based on a fully convolutional neural network of feature pyramid network (FPN) family. This network is consisted of pre-trained on ImageNet Resnet50 encoder and neatly developed decoder. Based on validation results, leaderboard score and our own experience this network shows reliable results for the DEEPGLOBE - CVPR 2018 land cover classification sub-challenge. Moreover, this network moderately uses memory that allows using GTX 1080 or 1080 TI video cards to perform whole training and makes pretty fast predictions

Protestants and Neo-Protestants in Post-Soviet Dagestan: Values Transformation in Missionary Practices (1990-2000)

The transformation in Protestants’ and neo-Protestants’ values in their missionary practices in the modern Dagestan is discussed. It is noted that the processes of globalization caused by the international situation, the unfolding socio-economic, political and spiritual depression in the country contributed to the “influx” of Western missionaries and the emergence of new non-traditional charismatic churches, allowing them to occupy its niche in republic religious structure for some time. On the basis of sociological survey the analysis of value views of the Dagestan Protestants in their Evangelical mission is carried out. It is shown that the religious well-being of the adepts of the studied communities is focused on proselytizing and missionary activity among the carriers of the other worldview. It is established that the Dagestan Protestants positively evaluate proselytism with the motivation for its non-violent and peaceful propagation while simultaneously ignoring the possibility of aggravation of inter-religious and inter-ethnic relations. It is revealed that the respondents have different points of view on the promotion of their faith among representatives of other religions. The authors conclude that, on the one hand, the proclamation of the principles of freedom of choice of a religious worldview, on the other - the aim to emphasize the exclusivity of their beliefs with the motivation of its “perfection and correctness” allow to make conclusion about the contradictory nature of the respondents’ opinions

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques. [Abstract copyright: Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.