Pancreatic cancer pathological image classification method based on self-attention feature fusion

Abstract

The invention provides a pancreatic cancer pathological image classification method based on self-attention feature fusion, and the method comprises the steps: firstly, employing a convolutional neural network model to extract the features of an input image, and carrying out the feature embedding of a feature map outputted by the final stage of the convolutional neural network model; secondly, feature maps output by the convolutional neural network model at different stages are subjected to attention analysis to obtain attention guidance information; then, a Transform model based on self-attention feature modeling and a self-attention feature fusion network model are constructed; and finally, training the self-attention feature fusion network model for multiple rounds, measuring and determining the model corresponding to the round with the optimal result by using the pathological image of the verification set, thereby constructing a pancreatic cell cancerization classification diagnosis system, and judging whether the pancreatic cell pathological image is a pancreatic cancer cell image or a normal cell image through the system. According to the invention, global modeling is carried out on the convolutional neural network features by applying a self-attention technology and an attention analysis mechanism so as to realize high-precision rapid on-site evaluation of pancreatic cancer.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence-assisted pathological image recognition, in particular to a method for classifying pancreatic cancer pathological images based on self-attention feature fusion. Background technique [0002] As a common malignant tumor of the digestive system, pancreatic cancer has the characteristics of difficult early diagnosis, poor prognosis and high degree of malignancy. With the improvement of residents' living standards, the incidence of pancreatic cancer is also increasing year by year. Since its five-year survival rate is only 6%, it is now one of the cancers with the highest mortality rate in the world. In order to realize the early screening and high-precision diagnosis of pancreatic cancer, the endoscopic ultrasound-guided fine-needle aspiration biopsy (EUS-FNA) technology based on endoscopic ultrasound guidance (EUS-FNA) has been proposed in recent years. Rapid on-site evaluation (ROS...

AI Technical Summary

Problems solved by technology

Although a series of studies have proved its great advantages in the field of image processing, the Transformer series models require a large amount of data to fully exploit the potential of the self-attention mechanism

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