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Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ
Purpose: To determine whether deep learning-based algorithms applied to ...
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Performance assessment of the deep learning technologies in grading glaucoma severity
Objective: To validate and compare the performance of eight available de...
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A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants
Early quantification of Tuta absoluta pest's effects in tomato plants is...
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Recommending Metamodel Concepts during Modeling Activities with Pre-Trained Language Models
The design of conceptually sound metamodels that embody proper semantics...
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Application of Deep Learning in Neuroradiology: Automated Detection of Basal Ganglia Hemorrhage using 2D-Convolutional Neural Networks
Background: Deep learning techniques have achieved high accuracy in imag...
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Neurological Status Classification Using Convolutional Neural Network
In this study we show that a Convolutional Neural Network (CNN) model is...
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Cost-Effective Training of Deep CNNs with Active Model Adaptation
Deep convolutional neural networks have achieved great success in variou...
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Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients
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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