Gisso Oreo, Sonya Feng, Ningning Guo, Heidi Warpack, Grissel Hernandez, Kathleen Bradley
Inclusion of Implicit Bias in Health Equity Education Design: A New Mandate for Continuing Education
Journal of continuing education in nursing
cervical cell diagnostics autonomous screening technology constitutes foundation automated diagnostic systems currently numerous deep learningbased classification techniques successfully implemented analysis cervical cell images yielding favorable outcomes nevertheless efficient discrimination cervical cells continues challenging due large intraclass small interclass variations key dealing problem capture localized informative differences cervical cell images represent discriminative features efficiently existing methods neglect importance global morphological information resulting inadequate feature representation capability address limitation propose novel cervical cell classification model focuses purified fusion information specifically first integrate detailed texture information morphological structure features named cervical pathology information fusion second order enhance discrimination cervical cell features address data redundancy bias inherent fusion design cervical purification bottleneck module model strikes balance leveraging purified features facilitating highefficiency discrimination furthermore intend unveil intricate cervical cell dataset cervical cytopathology image dataset ccid extensive experiments two realworld datasets show proposed model outperforms stateoftheart cervical cell classification models results show method well help pathologists accurately evaluate cervical smears