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https://www.selleckchem.com/products/bms-927711.html Postoperative pathological examination revealed no local recurrent tumor at the ESD site in the stomach. Swollen lymph node was diagnosed as metastasis and lymph node metastasis was limited near the cardia. This case provides valuable information about tumor with a minimum poorly differentiated adenocarcinoma component may develop lymph node metastasis even satisfying the guidelines criteria for curative resection. This case provides valuable information about tumor with a minimum poorly differentiated adenocarcinoma component may develop lymph node metastasis even satisfying the guidelines criteria for curative resection.Magnetic resonance imaging (MRI) systems and their continuous, failure-free operation is crucial for high-quality diagnostics and seamless workflows. One important hardware component is coils as they detect the magnetic signal. Before every MRI scan, several image features are captured which represent the used coil's condition. These image features recorded over time are used to train machine learning models for classification of coils into normal and broken coils for faster and easier maintenance. The state-of-the-art techniques for classification of time series involve different kinds of neural networks. We leveraged sequential data and trained three models, long short-term memory (LSTM), fully convolutional network (FCN), and the combination of those called LSTMFCN as reported by Karim et al. (IEEE access 61662-1669, 2017). We found LSTMFCN to combine the benefits of LSTM and FCN. Thus, we achieved the highest F1-score of 87.45% and the highest accuracy of 99.35% using LSTMFCN. Furthermore, we tackled the high data imbalance of only 2.1% data collected from broken coils by training a Gaussian process (GP) regressor and adding predicted sequences as artificial samples to our broken labelled data. Adding 40 synthetic samples increased the classification results of LSTMFCN to an F1-score of 92.30

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