Alexander Kalinovsky

United Institute of Informatics Problems | UIIP NASB · Biomedical Image Analysis

This paper presents results of the first, exploratory stage of research and developments on segmentation of lungs in X-Ray chest images (Chest Radiographs) using Deep Learning methods and Encoder-Decoder Convolutional Neural Networks (ED-CNN). Computational experiments were conducted using GPU Nvidia TITAN X equipped with 3072 CUDA Cores and 12Gb o...

This paper is devoted to the key issues associated with handling of the content of very large databases of natively digital medical images. A particular attention is drawn to the problem of examining image content in order to generate new knowledge, which is lately referred to as the image mining problem. Other important questions discussed in the...

Everyone wants to automate routine or tiring work. There are a lot of tasks that may be done by drones. For example border protection or delivering. But before any company or even country adopts any technology we need to verify that it's not vulnerable to any attacks. A satellite navigation is at least one vulnerability for drones, which can be eas...

The ability to predict protein complexes is important for applications in drug design and generating models of high accuracy in the cell. Recently deep learning techniques showed a significant success in protein structure prediction, but a protein docking problem is unsolved yet. We developed a two-staged approach which consists of deep convolution...

Structural prediction of protein-protein complexes has important application in such domains as modeling of biological processes and drug design. Homodimers (complexes which consist of two identical proteins) are the most common type of protein complexes in nature but there is still no universal algorithm to predict their 3D structures. Experimenta...

Image segmentation is a widely used technique to select a region of interest for further work. To assess segmentation impact on the accuracy of classification we used lungs segmentation in the classification of chest X-Ray images by subjects' age groups. Testing of different combinations of segmentation and classification methods showed that the us...

Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with...

Recently, due to a number of demonstrated top-ranked achievements the deep learning methods and convolutional neural networks (CNNs) are of high interest for medical image analysis community. The purpose of this study is to compare abilities of CNNs and conventional methods on the specific benchmarking task of image classification and prediction of...

Purpose Automatic detection of lung lesions is a complicated problem due to a large variety of lesion types. Lung lesions could be very different in size (e.g., nodules and lung masses). They may have different location and different internal structure. For instance, the internal structure of lung cancer tumors looks like a solid neoplasm whereas l...

In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algor...

In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algor...

This paper presents results that were obtained in comparative study of the efficiency of conventional and Deep Learning methods on the problem of predicting subjects' age by their chest radiographs. A large study group consisting of chest radiographs of 10 000 people was created by random sub-sampling of suitable subjects from the input image repos...

This paper present results of the use of Deep Learning approach and Convolutional Neural Networks (CNN) for the problem of breast cancer diagnosis. Specifically, the main goal of this particular study was to detect and to segment (i.e. delineate) regions of micro-and macro-metastases in whole-slide images of lymph node sections. The whole-slide ima...

Locally asymptotic estimation is derived from telemetry data processing by means of topological nonlinear method of temporal localization. Convergence for the function of topological instability at changing dimensionality is attained, and high reliability of diagnosis in a case of emergency caused by failure of equipment unit is proved. The essenti...