A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Summary

Here, a new model for thyroid nodule detection in ultrasound images is proposed, which uses Swin Transformer as the backbone to perform long-range context modeling. Experiments prove that it performs well in terms of sensitivity and accuracy.

Abstract

In recent years, the incidence of thyroid cancer has been increasing. Thyroid nodule detection is critical for both the detection and treatment of thyroid cancer. Convolutional neural networks (CNNs) have achieved good results in thyroid ultrasound image analysis tasks. However, due to the limited valid receptive field of convolutional layers, CNNs fail to capture long-range contextual dependencies, which are important for identifying thyroid nodules in ultrasound images. Transformer networks are effective in capturing long-range contextual information. Inspired by this, we propose a novel thyroid nodule detection method that combines the Swin Transformer backbone and Faster R-CNN. Specifically, an ultrasound image is first projected into a 1D sequence of embeddings, which are then fed into a hierarchical Swin Transformer.

The Swin Transformer backbone extracts features at five different scales by utilizing shifted windows for the computation of self-attention. Subsequently, a feature pyramid network (FPN) is used to fuse the features from different scales. Finally, a detection head is used to predict bounding boxes and the corresponding confidence scores. Data collected from 2,680 patients were used to conduct the experiments, and the results showed that this method achieved the best mAP score of 44.8%, outperforming CNN-based baselines. In addition, we gained better sensitivity (90.5%) than the competitors. This indicates that context modeling in this model is effective for thyroid nodule detection.

Introduction

The incidence of thyroid cancer has increased rapidly since 1970, especially among middle-aged women¹. Thyroid nodules may predict the emergence of thyroid cancer, and most thyroid nodules are asymptomatic². The early detection of thyroid nodules is very helpful in curing thyroid cancer. Therefore, according to current practice guidelines, all patients with suspected nodular goiter on physical examination or with abnormal imaging findings should undergo further examination^3,4.

Thyroid ultrasound (US) is a common method used to detect a....

Protocol

This retrospective study was approved by the institutional review board of the West China Hospital, Sichuan University, Sichuan, China, and the requirement to obtain informed consent was waived.

1. Environment setup

1. Graphic processing unit (GPU) software
   1. To implement deep learning applications, first configure the GPU-related environment. Download and install GPU-appropriate software and drivers from the GPU's website.
      ​NOTE: See the Table of Materials for those used in this study.
2. Python3.8 installation
   1. Open a terminal on the mac....

Results

The thyroid US images were collected from two hospitals in China from September 2008 to February 2018. The eligibility criteria for including the US images in this study were conventional US examination before biopsy and surgical treatment, diagnosis with biopsy or postsurgical pathology, and age ≥ 18 years. The exclusion criteria were images without thyroid tissues.

The 3,000 ultrasound images included 1,384 malignant and 1,616 benign nodules. The majority (90%) of the malignant nodules were .......

Discussion

This paper describes in detail how to perform the environment setup, data preparation, model configuration, and network training. In the environment setup phase, one needs to pay attention to ensure that the dependent libraries are compatible and matched. Data processing is a very important step; time and effort must be spent to ensure the accuracy of the annotations. When training the model, a "ModuleNotFoundError" may be encountered. In this case, it is necessary to use the "pip install" command to inst.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
GPU RTX3090	Nvidia	1	24G GPU
mmdetection2.11.0	SenseTime	4	https://github.com/open-mmlab/mmdetection.git
python3.8	—	2	https://www.python.org
pytorch1.7.1	Facebook	3	https://pytorch.org