Data annotation, model training and image processing method and device and storage medium

Abstract

The invention provides a lung focus detection algorithm based on weak annotation data. The algorithm comprises the steps of data labeling, model training and image processing, wherein a partially labeled weak data labeling method is adopted for labeling, a labeling result is perfected by referring to the thought of difficult case mining in the data labeling process, and it is guaranteed that a high-quality labeled data set is provided for subsequent model training under the condition that only part of focuses are labeled. According to the method, model training is combined with a 'weak data 'marking mode, positive and negative sample anchor points for network training are acquired from positive and negative sample sets respectively, and the focal loss hyper-parameters are adjusted according to the source characteristics of the positive and negative sample anchor points, so that the detection model obtains a better detection effect.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a training method for an image processing model and a method for processing images using the model. Background technique [0002] Lung cancer is one of the malignant tumors with the fastest growing morbidity and mortality, which has posed a great threat to the health and life of the population. The survival rate of lung cancer is highly correlated with the stage of the disease when it is first diagnosed. If it can be detected at an early stage, the 5-year survival rate can reach 70% to 90%. Compared with other cancers, the biological characteristics of lung cancer are very complex, and most of them have no obvious symptoms in the early stage. Nearly 75% of the people are found in the middle and late stages, and the treatment cost is high and the effect is not good. Therefore, early detection and diagnosis of lung cancer is particularly important. Lung cancer usually presents as ...

AI Technical Summary

Problems solved by technology

But one of the more difficult types is diffuse multiple lesions, which often occupy most of the lung space, and the thousands of lesions make labeling difficult

There are three reasons for this. 1. Due to the large number of lesions, finding and labeling all lesions will bring the risk of missing labels (such as diffuse infection of the whole lung, such as diffuse increased density); 2. Marking all lesions is very important for labeling Workers are challenged

3. The number of some types of lesions may not be too many, but it is very difficult to find them with the naked eye (typically, tiny nodules below 3mm)

These three reasons together lead to the occurrence of missing labels of lesions, which is difficult to avoid in the actual labeling process

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