A5048534198- Cutaneous Melanoma Detection and Management
- AI in cancer detection
- Cell Image Analysis Techniques
- Digital Imaging for Blood Diseases
- Medical and Biological Sciences
- Radiomics and Machine Learning in Medical Imaging
- Healthcare Systems and Public Health
- Information Systems and Technology Applications
- Microbial Inactivation Methods
- Enzyme Structure and Function
- melanin and skin pigmentation
- Artificial Intelligence in Education
- Advanced Scientific Research Methods
- Advanced Proteomics Techniques and Applications
- Microfluidic and Bio-sensing Technologies
- Nanowire Synthesis and Applications
- Technology and Human Factors in Education and Health
- Image Processing Techniques and Applications
- Skin Protection and Aging
- Mass Spectrometry Techniques and Applications
- Advanced Computational Techniques in Science and Engineering
- Advanced Data Processing Techniques
- Electrohydrodynamics and Fluid Dynamics
- Infrared Thermography in Medicine
- Optical Coherence Tomography Applications
National Research Nuclear University MEPhI
2018-2022
Ministry of Health of the Russian Federation
2018
The method of light microscopy is widely used in cancer diagnosis. result visual analysis subjective, depending particular on the experience morphologist. Preparation a qualified specialist takes up to 10 years. use artificial intelligence methods medical diagnostics (knowledge bases, expert systems, pattern recognition, this case Decision Support System with "HISTOLOGICAL ANALYSIS OF THYROID TUMORS" knowledge base), helps improve objectivity and accuracy paper discusses creation application...
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Abstract One of the most effective solutions oncology problems is creation artificial intelligence medical systems. Intelligent system a collaboration product doctors and IT specialists. This article reveals an approach to educational intelligent systems using example intellectual teaching for cytological diagnosis breast diseases. These are based on many years doctor’s practice improve quality training new doctors.
Предложен алгоритм анализа характеристик пигментной сети новообразований кожи. Он базируется на оценке коэффициента отклонения средних длин сегментов в локальных областях новообразования от среднего значения линий по всей области новообразования. Применение алгоритма позволяет отличать типичную пигментную сеть атипичной. Атипичная пигментная является существенным признаком определении меланомы ранних стадиях. Алгоритм может быть использован автоматизированных системах поддержки принятия...