![Evaluating the clinical applicability of neural networks for meningioma tumor segmentation on 3D MRI](/rails/active_storage/representations/proxy/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBBaWdSIiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--bb1a6945148f009dcc7ac61140a1b60b0b13b869/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaDdCem9MWm05eWJXRjBTU0lJY0c1bkJqb0dSVlE2QzNKbGMybDZaVWtpRFRZd01IZzJNREErQmpzR1ZBPT0iLCJleHAiOm51bGwsInB1ciI6InZhcmlhdGlvbiJ9fQ==--33b2b080106a274a4ca568f8742d366d42f20c14/Figure-6-Sreedhar-JEI-23-265_R2.png)
Authors emphasize the challenges of manual tumor segmentation and the potential of deep learning models to enhance accuracy by automatically analyzing MRI scans.
Read More...Evaluating the clinical applicability of neural networks for meningioma tumor segmentation on 3D MRI
Authors emphasize the challenges of manual tumor segmentation and the potential of deep learning models to enhance accuracy by automatically analyzing MRI scans.
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