As medical follow-up work is more and more valued, the task of obtaining information related to the follow-up guidance through medical image analysis has become increasingly important. However, most deep learning-based methods are not suitable for dealing with such task. In order to solve the problem, a Multi-term Knowledge Distillation (MKD) model was proposed. Firstly, with the advantage of knowledge distillation in model transfer, the classification task with long-term follow-up information was converted into a model transfer task based on domain knowledge. Then, the follow-up knowledge contained in the long-term medical images was fully utilized to realize the long-term classification of lung nodules. At the same time, facing the problem that the data collected during the follow-up process were relatively unbalanced every year, a meta-learning method based normalization method was proposed, and therefore improving the training accuracy of the model in the semi-supervised mode effectively. Experimental results on NLST dataset show that, the proposed MKD model has better classification accuracy in the task of long-term lung nodule classification than the deep learning classification models such as GoogleNet. When the amount of unbalanced long-term data reaches 800 cases, the MKD enhanced by meta-learning method can improve the accuracy by up to 7 percentage points compared with the existing state-of-the-art models.

Aiming at the problem of high misdiagnosis rate, high false positive rate and low detection accuracy in pulmonary nodule computer-aided detection, a method of nodular detection based on three-dimensional shape index and Hessian matrix eigenvalue was proposed. Firstly, the parenchyma region was extracted and the eigenvalues and eigenvectors of the Hessian matrix were calculated. Secondly, the three-dimensional shape index formula was deduced by the two-dimensional shape index, and the improved three-dimensional spherical like filter was constructed. Finally, in the parenchyma volume, the suspected nodule region was detected, and more false-positive regions were removed. The nodules were detected by the three-dimensional volume data, and the detected coordinates were input as the seeds of belief connect, and the three-dimensional data was splited to pick out three-dimensional nodules. The experimental results show that the proposed algorithm can effectively detect different types of pulmonary nodules, and has better detection effect on the ground glass nodules which are more difficult to detect, reduces the false positive rate of nodules, and finally reaches 92.36% accuracy rate and 96.52% sensitivity.

From the perspective of revenue management, a new approach for order acceptance under uncertainty in Make-to-Oder (MTO) manufacturing using average-reward reinforcement learning was proposed. In order to maximize the average expected revenue, the proposed approach took order types and different combinations of price and leadtime as criteria for the classification of the system states based on multi-level pricing mechanism. The simulation results show that the proposed algorithm has learning and selective ability to accept the order. Comparisons made with other order acceptance policies show the effectiveness of the proposed algorithm in average revenue, accepted order types, and adaptability.