May 9, 2024, 4:41 a.m. | Fakrul Islam Tushar, Avivah Wang, Lavsen Dahal, Michael R. Harowicz, Kyle J. Lafata, Tina D. Tailor, Joseph Y. Lo

cs.LG updates on

arXiv:2405.04605v1 Announce Type: cross
Abstract: BACKGROUND: Lung cancer's high mortality rate can be mitigated by early detection, which is increasingly reliant on artificial intelligence (AI) for diagnostic imaging. However, the performance of AI models is contingent upon the datasets used for their training and validation. METHODS: This study developed and validated the DLCSD-mD and LUNA16-mD models utilizing the Duke Lung Cancer Screening Dataset (DLCSD), encompassing over 2,000 CT scans with more than 3,000 annotations. These models were rigorously evaluated against …

abstract ai models artificial artificial intelligence arxiv benchmarking cancer cs.lg ct scan datasets detection diagnostic health however imaging intelligence lung cancer mortality multiple performance rate training type validation

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