End-to-end Lung Nodule Detection in Computed Tomography​​

•As there is ever-increasing need for annotated training samples to feed the learning-based models (such as deep convolutional networks), we developed a multi-stage self-paced learning framework to increase the samples by tens of folds, based on the refinement of the unlabeled samples. Those increased amount of training samples, which will be very expensive both in cost and labor work, can lead to more accurate and robust AI programs.

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