Thyroid nodule semi-supervised segmentation method based on attention mechanism

Abstract

The invention discloses a thyroid nodule semi-supervised segmentation method based on an attention mechanism. The method comprises the following steps of: 1, carrying out preprocessing of a thyroid ultrasonic image, and removing an edge information region in the image; 2, constructing a semi-supervised segmentation neural network, performing classification and segmentation prediction tasks on theultrasonic image, and adjusting a network structure to adapt to a specific application scene; 3, adding an attention mechanism into the semi-supervised segmentation neural network to improve the network effect; 4, measuring the performances of a semi-supervised segmentation algorithm and an existing full-supervised segmentation algorithm in the field of thyroid nodule auxiliary diagnosis through an intersection-parallel ratio and a Dice coefficient; and 5, continuously reducing the number of the pixel-level labels, and observing the change condition of the network performance. According to theinvention, the thyroid nodule semi-supervised segmentation method based on an attention mechanism benefits from the semi-supervised effect of a small number of pixel-level labels while keeping the high segmentation performance of the semi-supervised segmentation model, learns the real benign and malignant characteristics of the nodules and improves the benign and malignant classification capacity.

Description

technical field [0001] The invention belongs to the fields of deep learning, computer-aided medical treatment and medical image processing, and relates to neural network classification technology and semi-supervised learning technology, especially a semi-supervised segmentation method for thyroid nodules based on an attention mechanism. Background technique [0002] The research and application of medical aided diagnosis using deep learning methods covers skin diseases, brain diseases, lung inflammation, and thyroid nodules. Jinlian Ma et al. [1] first used convolutional neural networks in the ultrasound diagnosis of thyroid nodules in 2017. They fine-tuned two networks pre-trained in the ImageNet database by training separately, and achieved a diagnostic accuracy of 83.02% ± 0.72% by concatenating feature maps. However, its pre-trained network is based on ImageNet, a natural scene image dataset, and its pre-trained features are mostly natural scenes, not pathological featu...

Images