A modified Y-Net architecture based on the VGG11 encoder is used to simultaneously learn geometric orientation (similarity transform parameters) of the chest and segmentation of radiographic annotations.
The contributions of this work are threefold: 1) To demonstrate that neural networks can be efficient aggregators of whole families of parameteric solutions to physical problems, trained using data created with traditional, trusted numerical methods such as finite elements.
This paper presents a state-of-the-art model for CXR abnormality detection, reaching an average AUROC of 0. 91.
Machine translation software has seen rapid progress in recent years due to the advancement of deep neural networks.
In this work, (i) we show that models trained using single-step adversarial training method learn to prevent the generation of single-step adversaries, and this is due to over-fitting of the model during the initial stages of training, and (ii) to mitigate this effect, we propose a single-step adversarial training method with dropout scheduling.
Radar for indoor monitoring is an emerging area of research and development, covering and supporting different health and wellbeing applications of smart homes, assisted living, and medical diagnosis.
We also introduce a sequence constraint in the output of an MLSSL classifier to guarantee the sequential pattern in the predictions.
The model performance is compared to that of 157 dermatologists from 12 university hospitals in Germany based on MClass-D dataset.