Tobias Bensel, Simon Megiroo, Werner Kronenberg, Wolfgang Bömicke, Timo Ulrichs, Sebastian Hinz
Oral Health Status of Healthcare Workers in Ilembula/Tanzania during the COVID-19 Condition
Healthcare (Basel, Switzerland)
deep learningbased methods promote save critical time diagnosis pneumonia computed tomography ct images chest methods usually rely large amounts labeled data learn good visual representations however medical images difficult obtain need labeled professional radiologists address issue novel contrastive learning model token projection namely cotp proposed improving diagnostic quality fewshot chest ct images specifically 1 utilize solely unlabeled data fitting cotp along small number labeled samples finetuning 2 present new omicron dataset modify data augmentation strategy ie random poisson noise perturbation ct interpretation task 3 token projection utilized improve quality global visual representations resnet50 pretrained cotp attained accuracy acc 9235 sensitivity sen 9296 precision pre 9154 area receiveroperating characteristics curve auc 9890 presented omicron dataset contrary resnet50 without pretraining achieved acc sen pre auc 7761 7790 7669 8566 respectively extensive experiments reveal model pretrained cotp greatly outperforms without pretraining cotp improve efficacy diagnosis reduce heavy workload radiologists screening omicron pneumonia