Leah Elizabeth Chapman, Wendi Gosliner, Deborah A Olarte, Lorrene D Ritchie, Marlene B Schwartz, Michele Polacsek, Christina E Hecht, Kenneth Hecht, Lindsey Turner, Anisha I Patel, Monica Daniela Zuercher, Margaret Read, Tara P Daly, Juliana Fw Cohen
Universal School Meals During the Pandemic: A Mixed Methods Analysis of Parent Perceptions from California and Maine
Journal of the Academy of Nutrition and Dietetics
progress incorporating deep learning field medical image interpretation greatly hindered due tremendous cost time associated generating ground truth supervised machine learning alongside concerns inconsistent quality images acquired active learning offers potential solution problems expanding dataset ground truth algorithmically choosing informative samples ground truth labeling still effort incurs costs human labeling needs minimization furthermore automatic labeling approaches employing active learning often exhibit overfitting tendencies selecting samples closely aligned training set distribution excluding outofdistribution samples could potentially improve models effectiveness propose majority outofdistribution instances attributed inconsistent cross images since fda approved first wholeslide image system medical diagnosis 2017 wholeslide images provided enriched critical information advance field automated histopathology exemplify benefits novel deep learning strategy utilizes highresolution wholeslide microscopic images quantitatively assess visually highlight inconsistencies within wholeslide image dataset employed study accordingly introduce deep learningbased preprocessing algorithm designed normalize unknown samples training set distribution effectively mitigating overfitting issue consequently approach significantly increases amount automatic regionofinterest ground truth labeling highresolution wholeslide images using active deep learning accept 92 automatic labels generated unlabeled data cohort expanding labeled dataset 845 additionally demonstrate expert time savings 96 relative manual expert groundtruth labeling