{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Science Tech Brief By HackerNoon","title":"Deep Neural Networks to Detect and Quantify Lymphoma Lesions: Materials and Methods","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/a6bbc76a\"></iframe>","width":"100%","height":180,"duration":805,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/deep-neural-networks-to-detect-and-quantify-lymphoma-lesions-materials-and-methods.\n             This study performs comprehensive evaluation of four neural network architectures for lymphoma lesion segmentation from PET/CT images. \n            Check more stories related to science at: https://hackernoon.com/c/science.\n            You can also check exclusive content about #positron-emission-tomography, #computed-tomography, #deep-learning, #segmentation, #detection, #lesion-measures, #intra-observer-variability, #inter-observer-variability,  and more.\n            \n            \n            This story was written by: @reinforcement. Learn more about this writer by checking @reinforcement's about page,\n            and for more stories, please visit hackernoon.com.\n            \n                \n                \n                This study performs comprehensive evaluation of four neural network architectures for lymphoma lesion segmentation from PET/CT images.\n        \n        ","thumbnail_url":"https://img.transistorcdn.com/S66fL9skYMhlajDauLWqBH_bXds_u8JsPbvAZlh45OA/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMjczLzE2ODM1/ODI0MjQtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}