Valérie Brisson, Constance Fournier, Alicia Pelletier, Marilyne Joyal, Lyne Defoy, Pascale Tremblay
Vocal Health and Vocal Health Knowledge Among Occupational Voice Users in the Province of Quebec
Journal of voice : official journal of the Voice Foundation
establish multimodal model distinguishing benign malignant breast lesions clinical data mammography mri images including t2wi diffusionweighted images dwi apparent diffusion coefficient adc dcemri images 132 benign breast cancer patients analyzed retrospectively region interest roi image marked segmented using matlab software mammography t2wi dwi adc dcemri models based resnet34 network trained using integrated learning method five models used basic model voting methods used construct multimodal model dataset divided training set prediction set accuracy sensitivity specificity positive predictive value negative predictive value model calculated diagnostic efficacy model analyzed using receiver operating characteristic curve roc area curve auc diagnostic value determined delong test statistically significant differences set p 005 evaluated ability model classify benign malignant tumors using test set auc values multimodal model mammography model t2wi model dwi model adc model dcemri model 0943 0645 0595 0905 0900 0865 respectively diagnostic ability multimodal model significantly higher compared mammography t2wi models however compared dwi adc dcemri models significant difference diagnostic ability models deep learning model based multimodal image training practical value diagnosis benign malignant breast lesions