Evgeniy Pavlovskiy

Novosibirsk State University

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Bair Tuchinov

Novosibirsk State University

ЕЛ

Е. Л. Амелина

Novosibirsk State University

M. E. Amelin

Federal Neurosurgical Center Novosibirsk

Sergey Golushko

Novosibirsk State University

Vladimir Groza

Novosibirsk State University

A. Yu. Letyagin

Institute of Cytology and Genetics

Nikolay Tolstokulakov

Novosibirsk State University

Minh Sao Khue Luu

Novosibirsk State University

Roman M. Kenzhin

Boreskov Institute of Catalysis

Leyuan Sheng

Novosibirsk State University

Cooperative Institutions

Novosibirsk State University

Siberian Branch of the Russian Academy of Sciences

Russian Academy of Sciences

Federal Neurosurgical Center Novosibirsk

Institute of Computational Technologies

Novosibirsk Tuberculosis Research Institute

Institute of Petroleum Geology and Geophysics

Centre National de la Recherche Scientifique

Sobolev Institute of Mathematics

Aston University

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Data Preprocessing Via Compositions Multi-Channel MRI Images to Improve Brain Tumor Segmentation

Nikolay Tolstokulakov Evgeniy Pavlovskiy Bair Tuchinov Е. Л. Амелина M. E. Amelin

Magnetic resonance imaging (MRI) stays one of the most essential noninvasive methods for brain diagnostics. It allows obtaining the detailed 3D image of the brain, including various types of soft tissues. In this paper, we compare the influence of the multichannel data composition approach on the model's performance. We consider the binary brain tumor segmentation problem evaluating the Dice, Recall and Precision metrics. One common way to process the medical images with the use of neural networks is to use 2D slices as the input. In contrast to the RGB images, there are plenty of methods of how to combine the multi-channel MRI data structure into the common format for ML-based algorithms. After evaluating several possible combinations we demonstrate the most performance improvement by 6–7% in Dice & Recall metrics using the pseudo-RGB approach.

10.1109/isbiworkshops50223.2020.9153416

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Citations (3)

Harnessing Ensemble Machine Learning Models for Timely Diagnosis of Breast Cancer Metastasis: A Case Study on CatBoost, XGBoost, and LGBM

Minh Sao Khue Luu Santanu Banerjee Evgeniy Pavlovskiy Bair Tuchinov

Breast cancer metastasis

Ensemble Learning

10.1109/edm61683.2024.10615210

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FRiS-censoring of reference sample in face recognition task by deep neural networks

Sergey Alyamkin Nikita A. Nikolenko Evgeniy Pavlovskiy V. V. Dyubanov

To increase face recognition quality in video surveillance system an approach of censoring incoming photos based on FRiS function is presented.

Censoring (clinical trials)

Deep Neural Networks

10.1109/ssdse.2017.8071961

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Data Preprocessing via Multi-sequences MRI Mixture to Improve Brain Tumor Segmentation

Lecture notes in computer science (2020)

Vladimir Groza Bair Tuchinov Evgeniy Pavlovskiy Е. Л. Амелина M. E. Amelin

Brain tumor

Data pre-processing

10.1007/978-3-030-45385-5_62

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Citations (10)

S.A.I.ence 2020 Committees

М. П. Федорук Sergey Kabanikhin Sergey Yakovenko Sergei K. Turitsyn Ivan Bondarenko

10.1109/s.a.i.ence50533.2020.9303214

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Seismic Inversion for Fracture Model Reconstruction: From 1D Inversion to Machine Learning

Lecture notes in computer science (2023)

M. Protasov Roman M. Kenzhin Danil Dmitrachkov Evgeniy Pavlovskiy

10.1007/978-3-031-36808-0_7

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Reducing over-smoothness in speech synthesis using Generative Adversarial Networks

arXiv (Cornell University) (2018)

Leyuan Sheng Evgeniy Pavlovskiy

Speech synthesis is widely used in many practical applications. In recent years, speech synthesis technology has developed rapidly. However, one of the reasons why synthetic speech is unnatural is that it often has over-smoothness. In order to improve the naturalness of synthetic speech, we first extract the mel-spectrogram of speech and convert it into a real image, then take the over-smooth mel-spectrogram image as input, and use image-to-image translation Generative Adversarial Networks(GANs) framework to generate a more realistic mel-spectrogram. Finally, the results show that this method greatly reduces the over-smoothness of synthesized speech and is more close to the mel-spectrogram of real speech.

Spectrogram

Smoothness

Image translation

10.48550/arxiv.1810.10989

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Multi-class Brain Tumor Segmentation via Multi-sequences MRI Mixture Data Preprocessing

A. Yu. Letyagin Sergey Golushko M. E. Amelin Bair Tuchinov Е. Л. Амелина

In this paper, we extend the previous work on the robust pre-processing technique which allows to consider all available information from MRI scans by composition of T1, T1C and FLAIR sequences in the unique input. Such approach enriches the input data for the automatic segmentation process and helps to improve the accuracy of the segmentation performance. Proposed method also demonstrate significant improvement on the multi-class segmentation problem with respect to Dice metrics compare to similar training / evaluation procedure based on any single sequence regardless of the chosen neural network architecture. Obtained results demonstrate significant evaluation improvement while combining three MRI sequences in the 3-channel RGB like image for considered problem of multi-class brain tumor segmentation. We also provide results of comparison of various gradient descent optimization methods and of different backbone architectures. We found that different algorithms worked best for different tumors, but no single algorithm ranked in the top for all types of tumors simultaneously. Final improvements on the test part of our dataset are in the range of 6 - 9% on the trained model according to the Dice metric with the best value of 0.949.

Dice

10.1109/csgb51356.2020.9214645

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Citations (7)

Problems and Prospectives of Big Data Storage and Processing Standartization

2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) (2019)

Evgeniy Pavlovskiy

In the paper, we analyze the problem of standardization in the domain of storage and processing of big data in the application to the Internet of things. We highlight the underlying problems of big data; analyze the scientific, technological and economic barriers to the development of big data. We offer perspective research directions in the domain of development of standards of data representation.

Representation

Data Processing

External Data Representation

10.1109/sibircon48586.2019.8958046

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Citations (1)

Applying Variational Circuits in Deep Learning Architectures for Improving Discriminative Power of Speaker Embeddings

Lecture notes in networks and systems (2021)

Raphael Blankson Evgeniy Pavlovskiy

Discriminative model

Transfer of learning

10.1007/978-981-16-5348-3_38

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