Training method of pulmonary nodule detection model

Abstract

The invention relates to a training method of a pulmonary nodule detection model, and belongs to the technical field of medical image processing. The training method comprises the steps that an original CT image sample is acquired, the original CT image sample comprises a labeled sample and an unlabeled sample, the labeled sample is cut, and a pulmonary nodule block sample is obtained; training apositioning model according to the labeled samples; pre-training the classification model according to the pulmonary nodule block sample; sequentially inputting unlabeled samples into the trained positioning model and the pre-trained classification model, and classifying the pulmonary nodule blocks; and combining the classified pulmonary nodule blocks and pulmonary nodule block samples into training set data, and training the classification model again to obtain a final classification model. Unlabeled pulmonary nodule blocks are classified through the pre-trained classification model, more training samples are obtained, the classification model is trained through the more training samples, the labeling process is reduced, and the training efficiency of the pulmonary nodule detection modelis improved.

Description

technical field [0001] The invention relates to a training method for a pulmonary nodule detection model, which belongs to the technical field of medical image processing. Background technique [0002] Lung cancer is one of the most common malignant tumors in my country. The five-year survival rate of lung cancer in my country is only 15%. Early diagnosis of lung cancer is an important means to improve the survival rate of patients. However, pulmonary nodules, an early feature of lung cancer, are not obvious, and effective differential diagnosis and treatment of pulmonary nodules (referred to as pulmonary nodules) are needed. The diagnosis process is to quickly determine whether the pulmonary nodules are benign or malignant, and the treatment process is to remove the malignant nodules as soon as possible. Therefore, the diagnosis and treatment of pulmonary nodules can not only avoid unnecessary overtreatment, but also the key to the prevention and treatment of lung cancer. ...

AI Technical Summary

Problems solved by technology

[0006] The purpose of this application is to provide a training method for a pulmonary nodule detection model to solve the problem of insufficient sample data required for existing training

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