Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients

11/17/2020 ∙ by Daniel M. Lang, et al. ∙ 0 ∙ share

We investigated the ability of deep learning models for imaging based HPV status detection. To overcome the problem of small medical datasets we used a transfer learning approach. A 3D convolutional network pre-trained on sports video clips was fine tuned such that full 3D information in the CT images could be exploited. The video pre-trained model was able to differentiate HPV-positive from HPV-negative cases with an area under the receiver operating characteristic curve (AUC) of 0.81 for an external test set. In comparison to a 3D convolutional neural network (CNN) trained from scratch and a 2D architecture pre-trained on ImageNet the video pre-trained model performed best.

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Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients

Authors

Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ

Performance assessment of the deep learning technologies in grading glaucoma severity

A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants

Recommending Metamodel Concepts during Modeling Activities with Pre-Trained Language Models

Application of Deep Learning in Neuroradiology: Automated Detection of Basal Ganglia Hemorrhage using 2D-Convolutional Neural Networks

Neurological Status Classification Using Convolutional Neural Network

Cost-Effective Training of Deep CNNs with Active Model Adaptation

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