Výsledky vyhledávání - "Panfilov, Egor"

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Active Sensing of Knee Osteoarthritis Progression with Reinforcement Learning

Autor: Nguyen, Khanh, Nguyen, Huy Hoang, Panfilov, Egor, Tiulpin, Aleksei

Osteoarthritis (OA) is the most common musculoskeletal disease, which has no cure. Knee OA (KOA) is one of the highest causes of disability worldwide, and it costs billions of United States dollars to the global community. Prediction of KOA progressi

Externí odkaz: http://arxiv.org/abs/2408.02349

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End-To-End Prediction of Knee Osteoarthritis Progression With Multi-Modal Transformers

Autor: Panfilov, Egor, Saarakkala, Simo, Nieminen, Miika T., Tiulpin, Aleksei

Knee Osteoarthritis (KOA) is a highly prevalent chronic musculoskeletal condition with no currently available treatment. The manifestation of KOA is heterogeneous and prediction of its progression is challenging. Current literature suggests that the

Externí odkaz: http://arxiv.org/abs/2307.00873

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Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning

Autor: Panfilov, Egor, Saarakkala, Simo, Nieminen, Miika T., Tiulpin, Aleksei

Accurate prediction of knee osteoarthritis (KOA) progression from structural MRI has a potential to enhance disease understanding and support clinical trials. Prior art focused on manually designed imaging biomarkers, which may not fully exploit all

Externí odkaz: http://arxiv.org/abs/2201.10849

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Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation

Autor: Panfilov, Egor, Tiulpin, Aleksei, Klein, Stefan, Nieminen, Miika T., Saarakkala, Simo

Degeneration of articular cartilage (AC) is actively studied in knee osteoarthritis (OA) research via magnetic resonance imaging (MRI). Segmentation of AC tissues from MRI data is an essential step in quantification of their damage. Deep learning (DL

Externí odkaz: http://arxiv.org/abs/1908.04126

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