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In this paper, a non-stationary kernel is proposed which allows the surrogate model to adapt to functions whose smoothness varies with the spatial location of inputs, and a multi-level convolutional neural network (ML-CNN) is built for lung nodule classification whose hyperparameter configuration is optimized by using the proposed non-stationary kernel based Gaussian surrogate model.
DeepLung consists of two components, nodule detection (identifying the locations of candidate nodules) and classification (classifying candidate nodules into benign or malignant).
Ranked #4 on Lung Nodule Classification on LIDC-IDRI
In this paper, we propose a novel method to predict the malignancy of nodules that have the capability to analyze the shape and size of a nodule using a global feature extractor, as well as the density and structure of the nodule using a local feature extractor.
Ranked #5 on Lung Nodule Classification on LIDC-IDRI
Besides, empirical study shows that the reasoning process of learned networks is in conformity with physicians' diagnosis.
Ranked #1 on Neural Architecture Search on LIDC-IDRI