Cancer medical image data processing method, system and device and storage medium

Abstract

The invention discloses a cancer medical image data processing method, system, device and a storage medium. The method comprises the following steps: inputting a to-be-processed cancer medical image into a pre-established focus area segmentation model, obtaining a focus area image identification result output by the focus area segmentation model, filtering processing of different standards is carried out on the focus area image cut from the cancer medical image for multiple times, channel stacking is carried out, a channel stacking result is input into a pre-established feature extraction model, and a feature screening algorithm is used for screening out target features from the obtained pathological feature vectors; inputting the target characteristics into a multilayer perceptron to obtain cancer biomarker state index values output by the multilayer perceptron, and the like. According to the method, the focus area on the cancer medical image can be automatically identified, and the focus area image is cut out, so that the identification and cutting efficiency and accuracy are greatly improved. The method is widely applied to the technical field of image processing.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a cancer medical image data processing method, system, device and storage medium. Background technique [0002] In the field of cancer diagnosis and treatment, it is often necessary to analyze medical images such as CT and MRI of patients, locate lesions and extract features of lesions. However, the existing medical image analysis technology generally has the following problems: [0003] Before extracting the features of the lesion area, it is necessary to identify and cut out the lesion area of ​​the small target from the medical image to eliminate the interference of irrelevant information. At present, this process needs to be completed manually, and the efficiency is extremely low. Image processing, while the accuracy of cropping the lesion area is low; [0004] In the process of feature extraction, manual features are extracted using existing technologies s...

AI Technical Summary

Problems solved by technology

[0006] When using traditional networks such as unimproved UNet for image processing, it is easy to lose information during continuous sampling

[0007] The above problems have caused the analysis accuracy and efficiency of the prior art to be at a low level

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