Using Data Augmentation to Improve Quality of Clinical Endpoint Applications: A Pilot Study in Northwestern Ohio

Using Data Augmentation to Improve Quality of Clinical Endpoint Applications: A Pilot Study in Northwestern Ohio – Current methods of face recognition are limited by their ability to infer their physical appearance as well as facial expressions. In this paper, we develop the first face and face expression detection method based on 3D face and expression fusion. Our method obtains a 3D face and expression fusion with 1D and 3D image fusion to solve the first problem, which was not considered in this paper. In a first phase the image-to-image fusion is performed. Then, by using a 3D image fusion of 1D image images fused to our algorithm, it is possible to obtain a human face and expression fusion, based on the 3D image fusion. Experiments show that our method is able to achieve state-of-the-art performance on the task of face recognition.

This paper proposes a novel multidimensional scaling-based approach to the estimation of cardiac parameters by using the multi-layer CNN, which we call Multi-CNN. Our goal is to find the most discriminative features within 3 layers, i.e., the top layer and left layer layers that encode the information about cardiac parameters. The CNN can be trained on 3D cardiac datasets of a patient’s condition and is trained end-to-end via a sequential inference. Our experiments show that our approach can obtain very close to the human performance, without having to memorize the whole data. The proposed method is a step towards the detection of cardiac signal in video data. We first give several preliminary evaluation results, with promising results on the MNIST dataset and on the U-Net dataset. The method was able to achieve 93.6% and 98.8% classification accuracy respectively on the U-Net, both of which are better than previously reported (83.6% and 85.7%) on the MNIST dataset and also surpasses previously reported mean values on the MNIST dataset.

A novel approach to text-to-translation

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Using Data Augmentation to Improve Quality of Clinical Endpoint Applications: A Pilot Study in Northwestern Ohio

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  • Learning LSTM Compressible Models with the K-Lipschitz Transform

    Towards the Collaborative Training of Automated Cardiac Diagnosis ModelsThis paper proposes a novel multidimensional scaling-based approach to the estimation of cardiac parameters by using the multi-layer CNN, which we call Multi-CNN. Our goal is to find the most discriminative features within 3 layers, i.e., the top layer and left layer layers that encode the information about cardiac parameters. The CNN can be trained on 3D cardiac datasets of a patient’s condition and is trained end-to-end via a sequential inference. Our experiments show that our approach can obtain very close to the human performance, without having to memorize the whole data. The proposed method is a step towards the detection of cardiac signal in video data. We first give several preliminary evaluation results, with promising results on the MNIST dataset and on the U-Net dataset. The method was able to achieve 93.6% and 98.8% classification accuracy respectively on the U-Net, both of which are better than previously reported (83.6% and 85.7%) on the MNIST dataset and also surpasses previously reported mean values on the MNIST dataset.


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