Computer-aided model construction method based on deep learning gastric cancer pathological sections

Abstract

The invention discloses a computer aided model construction method based on deep learning gastric cancer pathological sections, and belongs to the technical field of artificial intelligence. The method uses a 121-layer dense-connected convolutional neural network to perform image recognition. A dense block structure in a DenseNet allows the high-level part of the network to acquire shallow features, which greatly reduces the over-fitting phenomenon. At the same time, the model has a large number of layers, which can fit more complex and smoother decision functions. Although the number of layers is large, the number of parameters of the model is not large, which saves resource consumption. In order to further avoid over-fitting, a training mechanism for migration learning is adopted. The model will be pre-trained on an ImageNet dataset to give the model a strong image feature extraction capability. The main optimization of the model during formal training can be better focused on how toextract the features of the diseased area, and the utilization rate of the data is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and mainly relates to a method for constructing a computer-aided recognition model of gastric cancer pathological slices based on a deep learning algorithm and using an attention mechanism to enhance the effect. Background technique [0002] Gastric cancer is the fourth most common cancer in the world, and its death rate is the second highest among all cancers. Therefore, gastric cancer has gradually become a public health problem of general concern. And if patients can be diagnosed in the early stage of gastric cancer tumors, then the treatment of gastric cancer patients will achieve significant results and greatly reduce the mortality of gastric cancer diseases. The images of gastric cancer pathological sections are the images obtained by obtaining gastric tissue through biopsy, staining the tissue sections with Hematoxylin & Eosin (H&E), and then taking pictures with a digital...

AI Technical Summary

Problems solved by technology

First of all, when performing pathological image segmentation and feature extraction, researchers are required to have professional knowledge in related fields, otherwise it is difficult to describe the morphological features, texture features and related structures in the sample slices

At the same time, pathological images are often very complex, and traditional methods may be difficult to extract distinguishing high-quality features, resulting in relatively general recognition accuracy

Images