Miao Jenny Hua, Gisela Butera, Oluwaseun Akinyemi, Deborah Porterfield
Biases and limitations in observational studies of Long COVID prevalence and risk factors: A rapid systematic umbrella review
PloS one
diagnosis outcome prediction gastric cancer gc machine learning methods based whole slide pathological images wsis shown promising performance reduced cost manual analysis nevertheless accurate prediction gc outcome may rely multiple modalities complementary information particularly gene expression data thus need develop multimodal learning methods enhance prediction performance paper collect dataset ruijin hospital propose multimodal learning method gc diagnosis outcome prediction called gacamml featured crossmodal attention mechanism perslide training scheme additionally perform feature attribution analysis via integrated gradient ig identify important input features proposed method improves prediction accuracy singlemodal learning method three tasks ie survival prediction 49 cindex pathological stage classification 116 accuracy lymph node classification 120 accuracy especially perslide strategy addresses issue high wsitopatient ratio leads much better results compared perperson training scheme interpretable analysis find although wsis dominate prediction samples still substantial portion samples whose prediction highly relies gene expression information study demonstrates great potential multimodal learning gcrelated prediction tasks investigates contribution wsis gene expression respectively shows model makes decision also provides insights association macroscopic pathological phenotypes microscopic molecular features