Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. All methods were evaluated us
The prediction of the traffic flow can give the people important traveling information. In this paper, the traffic flow prediction problem is studied. An ARIMA model is proposed for the traffic flow prediction. The ARIMA model is trained according to the different period traffic data. Based on the different period data training, the ARIMA model is refined more accuracy. The experiments show that the ARIMA model trained by the time-oriented data can reach a better result than the non time-oriented data trained model.
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