Amrit Baral, Vanessa Morales, Bria-Necole A Diggs, Yetunde Tagurum, Meghal Desai, Nawaf Alhazmi, Walter A Ramsey, Claudia Martinez, Denise C Vidot
Perceptions, Attitudes, and Knowledge of Cannabis and its Use: A Qualitative Study among Herbal Heart Study Young Adult Cannabis Consumers in South Florida
Preventive medicine reports
computeraided detection diagnosis systems cadecadx endoscopy commonly trained using highquality imagery representative heterogeneous input typically encountered clinical practice endoscopy image quality heavily relies skills experience endoscopist specifications system used screening factors poor illumination motion blur specific postprocessing settings significantly alter quality general appearance images socalled domain gap data used developing system data encounters deployment impact performance deep neural networks dnns supportive endoscopic cad systems remains largely unexplored many systems eg polyp detection already rolled clinical practice poses severe patient risks particularly community hospitals imaging equipment experience subject considerable variation therefore study aims evaluate impact domain gap clinical performance cadecadx various endoscopic applications leverage two publicly available data sets kvasirseg giana two inhouse data sets investigate performance commonlyused dnn architectures synthetic clinically calibrated image degradations prospectively collected dataset including 342 endoscopic images lower subjective quality additionally assess influence dnn architecture complexity data augmentation pretraining techniques improved robustness results reveal considerable decline performance 116 15 compared reference within clinically calibrated boundaries image degradations nevertheless employing advanced dnn architectures selfsupervised indomain pretraining effectively mitigate drop 77 203 additionally enhancements yield highest performance manually collected test set including images lower subjective quality comprehensively assessing robustness popular dnn architectures training strategies across multiple datasets study provides valuable insights performance limitations endoscopic applications findings highlight importance including robustness evaluation developing dnns endoscopy applications propose strategies mitigate performance loss