Slice defect detection method, electronic device and readable storage medium

Abstract

The invention relates to the technical field of intelligent decision making in artificial intelligence and discloses a slice defect detection method. The method comprises the following steps: inputting a to-be-detected slice set into a feature extraction branch of a slice defect detection model to obtain a feature set of each slice; inputting the feature set into an up-sampling branch of a slice defect detection model to obtain a defect area of each slice and a predicted defect category corresponding to the defect area; inputting the feature set into a classification branch of the slice defectdetection model to obtain a second defect category distribution table; and when a second probability value of the predicted defect category corresponding to each defect area in the second defect category distribution table is greater than a preset threshold, taking the predicted defect category corresponding to each defect area as a target defect category corresponding to each defect area. According to the invention, the accuracy of slice defect area and defect category detection is improved. Moreover, the invention also relates to a blockchain technology, and the method can be applied to thefield of smart medical treatment, thereby promoting the construction of smart cities.

Description

technical field [0001] The invention relates to the technical field of intelligent decision-making, in particular to a slice defect detection method, an electronic device and a readable storage medium. Background technique [0002] Medical image slices are of great significance to 3D positioning, 3D visualization, surgical planning, and computer-aided diagnosis. The quality of slices directly affects diagnostic efficiency and quality. High-quality slices are the basis and guarantee for correct pathological diagnosis. , in order to improve the quality of slices, it is usually necessary to detect and classify defect areas on defective slices for targeted improvement. [0003] At present, the u-net network is usually used to detect defects on defect slices. However, since the u-net network only uses convolutional layers and pooling layers, as the network deepens, information will gradually be lost, and gradients are prone to disappear or explode. In this case, the defect area ...

AI Technical Summary

Problems solved by technology

[0003] At present, the u-net network is usually used to detect defects on defect slices. However, since the u-net network only uses convolutional layers and pooling layers, as the network deepens, information will gradually be lost, and gradients are prone to disappear or explode. In this case, the defect area cannot be accurately identified; and the u-net network mainly classifies a single pixel, lacking the category detection of the whole image, which makes the defect classification accuracy not high

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