Cervical cell full slice classification method based on context modeling

Abstract

The invention discloses a cervical cell full slice classification method based on context modeling. The method comprises the following steps: 1, obtaining T-type cervical cell full slice samples; 2, constructing a Faster Rcnn network-based cell detection and feature extraction module, carrying out cell detection and feature extraction on the cervical cell full slice sample, and extracting features from cell nucleus images with fixed sizes to obtain feature sequences of the cell nucleus images in different types of cell full slices; 3, building a context modeling module fusing the bidirectional long-short-term memory network and the attention mechanism; 4, constructing a cervical cell full slice classifier; and 5, carrying out classification prediction on the cervical cell full slice. More effective learning is carried out on the information obtained after feature extraction is carried out on the cell nucleus images in the input different cervical cell full slices, accurate classification of multiple different cervical cell full slices is completed, and the cell level marking cost of a current cervical cell classification method can be effectively reduced.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to image classification technology, in particular to a method for classifying cervical cytopathological full slices based on context modeling. Background technique [0002] Traditional pathological image analysis is completed by pathologists manually reading under a microscope. This link requires doctors with rich clinical experience to analyze a large number of histopathological slice images. However, the number of scans that doctors read every day is very large. , The reading time is long, and doctors may seriously affect the accuracy of pathological slide analysis due to overloaded workload and subjective emotions, which has brought great obstacles to the development of pathological slide analysis. If it is possible to improve the automatic process of cervical cell slice classification and use computer-aided film reading, it will bring new development ideas to pa...

AI Technical Summary

Problems solved by technology

At present, most cervical cell classification methods based on deep learning only use simple network models to extract and learn cell image features, and there is still a lot of room for improvement in the accuracy of prediction.

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