Towards the Collaborative Training of Automated Cardiac Diagnosis Models


Towards the Collaborative Training of Automated Cardiac Diagnosis Models – 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.

This paper presents a novel technique for constructing an embedding of a sequence into two discrete-state-like domains at the same time. Using a set of image sequences, the embedding process is then divided into two sub-spaces, one for each of the embedding domains, and a new domain is automatically constructed from the new embedding domain. The embedding process is then performed on a new set of sequences representing different parts of the sequences (i.e., regions), using only some of them being visible. The proposed technique is evaluated on synthetic and real datasets and on real 3D objects. Results show that the proposed method significantly boosts the performance of 3D prediction, and can also be used for supervised 3D-ARs.

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Towards the Collaborative Training of Automated Cardiac Diagnosis Models

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  • Stochastic Optimization for Large Ensembles: Some Experiments

    Video Frame Interpolation with Spatial Recurrent Neural NetworksThis paper presents a novel technique for constructing an embedding of a sequence into two discrete-state-like domains at the same time. Using a set of image sequences, the embedding process is then divided into two sub-spaces, one for each of the embedding domains, and a new domain is automatically constructed from the new embedding domain. The embedding process is then performed on a new set of sequences representing different parts of the sequences (i.e., regions), using only some of them being visible. The proposed technique is evaluated on synthetic and real datasets and on real 3D objects. Results show that the proposed method significantly boosts the performance of 3D prediction, and can also be used for supervised 3D-ARs.


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