Alexander Kalinovsky
Impact in
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- Artificial Intelligence in Healthcare
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- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
Papers in
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- Lung Cancer Diagnosis and Treatment 1
- Chronic Obstructive Pulmonary Disease (COPD) Research 1
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- COVID-19 diagnosis using AI 2
- Radiomics and Machine Learning in Medical Imaging 1
- Co-authors
- Vassili Kovalev (4 shared papers)Vitali Liauchuk (2 shared papers)Yashin Dicente Cid (1 shared paper)Henning Müller (1 shared paper)
- Journals
- The international archives of the photogrammetry, remote sensing and spatial information sciences (1 paper)Digital Library of the Belarusian State University (Belarusian State University) (2 papers)CLEF (Working Notes) (1 paper)Репозиторий БГУИР (BSUIR Repository) (1 paper)
- Partner nations
- Belarus
In The Last Decade
Alexander Kalinovsky
5 papers receiving 53 citations
Peers
Comparison fields: 5 of 42
- Health Information Management 6
- Radiology, Nuclear Medicine and Imaging 20
- Computer Vision and Pattern Recognition 16
- Health Informatics 1
- Artificial Intelligence 22
Countries citing papers authored by Alexander Kalinovsky
This map shows the geographic impact of Alexander Kalinovsky's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Alexander Kalinovsky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander Kalinovsky more than expected).
Fields of papers citing papers by Alexander Kalinovsky
This network shows the impact of papers produced by Alexander Kalinovsky. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Alexander Kalinovsky. The network helps show where Alexander Kalinovsky may publish in the future.
Co-authors
The 4 scholars most cited alongside Alexander Kalinovsky, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Deep learning with theano, torch, caffe, tensorflow, and deeplearning4J: which one is the best in speed and accuracy? | 2016 | 34 |
| 2 | 2017 | 9 | |
| 3 | Lung image Ssgmentation using deep learning methods and convolutional neural networks | 2016 | 7 |
| 4 | Overview of the ImageCLEF 2017 Tuberculosis Task - Predicting Tuberculosis Type and Drug Resistances. | 2017 | 5 |
| 5 | Big medical data: image mining, retrieval, and analytics | 2015 | 3 |
About Alexander Kalinovsky
Alexander Kalinovsky is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging, Infectious Diseases, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 5 papers that have together received 58 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Lung Cancer Diagnosis and Treatment (1 paper), Tuberculosis Research and Epidemiology (1 paper), Chronic Obstructive Pulmonary Disease (COPD) Research (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper) and Advanced Neural Network Applications (1 paper). The work is most often cited by research in Health Information Management (6 citations), Radiology, Nuclear Medicine and Imaging (20 citations), Computer Vision and Pattern Recognition (16 citations), Health Informatics (1 citation) and Artificial Intelligence (22 citations). Alexander Kalinovsky has collaborated with scholars based in Belarus. Frequent co-authors include Vassili Kovalev, Vitali Liauchuk, Yashin Dicente Cid and Henning Müller. Their work appears in journals such as The international archives of the photogrammetry, remote sensing and spatial information sciences, Digital Library of the Belarusian State University (Belarusian State University), CLEF (Working Notes) and Репозиторий БГУИР (BSUIR Repository).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.