Onco-epidemiological features of head and neck tumors within the competence of the otorhinolaryngological service across the regions of the Republic of Belarus

Objective. To analyze the onco-epidemiological features of head and neck tumors within the competence of the otorhinolaryngological service across the regions of the Republic of Belarus.Materials and methods. The material for the study was data on 21,533 cases of malignant neoplasms of the head and neck (including laryngeal tumors) registered in the Belarusian Cancer Registry from 2009 to 2018.Results. Significant changes have occurred in the structure of the incidence of head and neck malignant neoplasms over the past decade. The leading positions are occupied by such tumors of visual localization as cancer of the oropharynx (14.9 %), the floor of the oral cavity (12.4 %), tonsils (11.4 %) and tongue (excluding the root of the tongue) (11.4 %), which are available for diagnosis during routine clinical examination.Conclusion. The analysis of newly diagnosed cases of malignant neoplasms depending on the localization has showed that regardless of the availability of otorhinolaryngologists and staffing levels, patients with primary manifestations of the tumor process are not timely referred to the health experts for morphological verification, which requires further organizational decisions on patient referral at different levels of health care and defining the role and scope of responsibility of subject-matter primary care specialists.

A Robust Invariant Local Feature Matching Method for Changing Scenes

The precise evaluation of camera position and orientation is a momentous procedure of most machine vision tasks, especially visual localization. Aiming at the shortcomings of local features of dealing with changing scenes and the problem of realizing a robust end-to-end network that worked from feature detection to matching, an invariant local feature matching method for changing scene image pairs is proposed, which is a network that integrates feature detection, descriptor constitution, and feature matching. In the feature point detection and descriptor construction stage, joint training is carried out based on a neural network. In the feature point extraction and descriptor construction stage, joint training is carried out based on a neural network. To obtain local features with solid robustness to viewpoint and illumination changes, the Vector of Locally Aggregated Descriptors based on Neural Network (NetVLAD) module is introduced to compute the degree of correlation of description vectors from one image to another counterpart. Then, to enhance the relationship between relevant local features of image pairs, the attentional graph neural network (AGNN) is introduced, and the Sinkhorn algorithm is used to match them; finally, the local feature matching results between image pairs are output. The experimental results show that, compared with the existed algorithms, the proposed method enhances the robustness of local features of varying sights, performs better in terms of homography estimation, matching precision, and recall, and when meeting the requirements of the visual localization system to the environment, the end-to-end network tasks can be realized.

Research on Target Localization Method of CRTS-III Slab Ballastless Track Plate Based on Machine Vision

In the construction of high-speed railway infrastructure, a CRTS-III slab ballastless track plate has been widely used. Anchor sealing is an essential step in the production of track plates. We design a novel automated platform based on industrial robots with vision guidance to improve the automation of a predominantly human-powered anchor sealing station. This paper proposes a precise and efficient target localization method for large and high-resolution images to obtain accurate target position information. To accurately update the robot’s work path and reduce idle waiting time, this paper proposes a low-cost and easily configurable visual localization system based on dual monocular cameras, which realizes the acquisition of track plate position information and the correction of position deviation in the robot coordinate system. We evaluate the repeatable positioning accuracy and the temporal performance of the visual localization system in a real production environment. The results show that the repeatable positioning accuracy of this localization system in the robot coordinate system can reach ±0.150 mm in the x- and y-directions and ±0.120° in the rotation angle. Moreover, this system completes two 18-megapixel image acquisitions, and the whole process takes around 570 ms to meet real production needs.

Causal inference regulates audiovisual spatial recalibration via its influence on audiovisual perception

To obtain a coherent perception of the world, our senses need to be in alignment. When we encounter misaligned cues from two sensory modalities, the brain must infer which cue is faulty and recalibrate the corresponding sense. We examined whether and how the brain uses cue reliability to identify the miscalibrated sense by measuring the audiovisual ventriloquism aftereffect for stimuli of varying visual reliability. To adjust for modality-specific biases, visual stimulus locations were chosen based on perceived alignment with auditory stimulus locations for each participant. During an audiovisual recalibration phase, participants were presented with bimodal stimuli with a fixed perceptual spatial discrepancy; they localized one modality, cued after stimulus presentation. Unimodal auditory and visual localization was measured before and after the audiovisual recalibration phase. We compared participants’ behavior to the predictions of three models of recalibration: (a) Reliability-based: each modality is recalibrated based on its relative reliability—less reliable cues are recalibrated more; (b) Fixed-ratio: the degree of recalibration for each modality is fixed; (c) Causal-inference: recalibration is directly determined by the discrepancy between a cue and its estimate, which in turn depends on the reliability of both cues, and inference about how likely the two cues derive from a common source. Vision was hardly recalibrated by audition. Auditory recalibration by vision changed idiosyncratically as visual reliability decreased: the extent of auditory recalibration either decreased monotonically, peaked at medium visual reliability, or increased monotonically. The latter two patterns cannot be explained by either the reliability-based or fixed-ratio models. Only the causal-inference model of recalibration captures the idiosyncratic influences of cue reliability on recalibration. We conclude that cue reliability, causal inference, and modality-specific biases guide cross-modal recalibration indirectly by determining the perception of audiovisual stimuli.

The state of cancer care in Russia: malignant tumors of the skin (C44). Prevalence, index of accuracy, localization and histological structure. Population-based research at the federal district level

Malignant tumors of the skin — visual localization, with a low mortality rate. Despite the fact that malignant tumors of the skin (C44) belong to the group of malignant tumors in many countries, cancer registries did not keep its records, the same attitude to this tumor was developed by doctors, which makes it difficult to conduct comparative studies between countries. When selecting data for the analysis of the prevalence of malignant tumors of the skin, in addition to the heading C44, a part of the heading ICD-10 — C46 — Kaposi's sarcoma — its part C46.0 — Kaposi's sarcoma of the skin is added. In many cases, it is not taken into account, due to its extremely rare occurrence, which does not have any practical impact on all the main analytical indicators. The purpose of this study is: for the first time in Russia, to consider not only the patterns of the prevalence of malignant tumors of the skin, but also to study the specifics of the localization and histological structure of this tumor localization, based on the newly created database of the population cancer Registry (PCR) of the North-Western Federal District of the Russian Federation (NWFD of the Russian Federation).

A Visual SLAM Robust against Dynamic Objects Based on Hybrid Semantic-Geometry Information

A visual localization approach for dynamic objects based on hybrid semantic-geometry information is presented. Due to the interference of moving objects in the real environment, the traditional simultaneous localization and mapping (SLAM) system can be corrupted. To address this problem, we propose a method for static/dynamic image segmentation that leverages semantic and geometric modules, including optical flow residual clustering, epipolar constraint checks, semantic segmentation, and outlier elimination. We integrated the proposed approach into the state-of-the-art ORB-SLAM2 and evaluated its performance on both public datasets and a quadcopter platform. Experimental results demonstrated that the root-mean-square error of the absolute trajectory error improved, on average, by 93.63% in highly dynamic benchmarks when compared with ORB-SLAM2. Thus, the proposed method can improve the performance of state-of-the-art SLAM systems in challenging scenarios.