Stochastic Gradient Descent with Step Cosine Warm Restarts for Pathological Lymph Node Image classification via PET/CT images

摘要

Warm restart strategies are widely used in gradient-free optimization to deal with multi-model functions. In this paper, we present a novel warm restart technique by step cosine function in stochastic gradient descent method that used to train a deep convolution neural network. Three variants of step cosine function with MobileNetv2 and ResNet50 network structure are tested in our pathological lymph node PET/CT dataset. Comparing to the step function as the warm restart schedule, the proposed step cosine warm restart strategy could improve the performance of pathological lymph node image classification in terms of accuracy, sensitivity and specificity, which increased at 2.1%, 0.7% and 2.9% with MobileNetv2, and at 1.3%, 1.4% and 1.3% with ResNet50, respectively.