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- W4292182770 abstract "Worldwide, knee joint complaints are most frequent in all age groups. Trauma, inflammation, infection, tumor, or aging that can damage the knee joint are detected with an MRI. MRI represents a standard technique for assessing knee joint anatomical structures, and it supports diagnosis, disease monitoring, or treatment planning. However, the reading and assessment of knee MRIs are time-consuming and can result in misdiagnosis. Therefore, it is crucial to elaborate intelligent and standardized methodologies of MRI investigation, to discover various knee lesions, increase diagnostic efficiency, and reduce bias or error in the evaluation due to human limitations such as fatigue, to name only one of them. This article reviews recent works that address the application of deep learning and discusses the related challenges in knee joint MRI analysis." @default.
- W4292182770 created "2022-08-18" @default.
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- W4292182770 date "2022-08-17" @default.
- W4292182770 modified "2023-09-24" @default.
- W4292182770 title "Challenges in Deep Learning Applied to the Knee Joint Magnetic Resonance Imaging: A Survey" @default.
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- W4292182770 doi "https://doi.org/10.1007/978-981-19-2397-5_42" @default.
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