Method and system for image classification of irregular cervical cell mass

Abstract

The invention discloses an irregular cervical cell cluster image classification method, which is characterized in that the method comprises the following steps: an off-line training sample set of a suspicious cell cluster is established, and a multi-resolution input three-channel neural network model is adopted to train a suspicious pathological cell cluster judgment model; Single cell cluster region was extracted from cervical pathological slice images, and the trained suspicious cell cluster decision model was used to determine the abnormality of each cell cluster. Mining the cell clusters that can not be judged correctly, as the training data input model focus on training. The invention takes the irregular cell cluster as the processing and identification unit, and utilizes the multi-resolution input three-channel neural network model to quickly identify the suspected pathological cell cluster, and simultaneously improves the identification accuracy and the identification efficiency.

Description

technical field [0001] The invention belongs to the field of medical cytopathological image processing, and more particularly relates to a method and system for classifying images of irregular cervical cell clusters. Background technique [0002] Cervical cancer is a malignant tumor with high incidence in women. Cervical liquid-based cytopathology is currently the most important means of preventing and screening cervical cancer. Accurate interpretation of diseased cells in cytopathological slice images is an important basis for doctors to determine the patient's condition and formulate a treatment plan. Since a cytopathological slide contains many fields of view, manual interpretation of cytopathological images is very time-consuming. Therefore, automatically and quickly identifying suspicious areas in slices for further interpretation by doctors can greatly improve the efficiency of doctors' diagnosis. This intelligent assisted film reading technology is of great signifi...

AI Technical Summary

Problems solved by technology

However, this block fusion scheme has the following disadvantages: the cells at the boundaries of the sub-blocks are artificially cut, reducing the recognition accuracy; too large a block is still difficult to deal with directly, and too small a block brings more boundary problems and increases processing overhead ;Inconsistency in processing results of adjacent blocks at subblock boundaries

Although traditional image processing and feature extraction methods can be used to identify suspicious lesion areas in slices, traditional image features or custom image features are difficult to completely characterize cell morphology, and usually only part of the cell morphology can be described, such as nuclear size, depth, etc. dyed

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