MsGoF：基于多尺度渐变顺序融合框架的超声图像乳腺病变分类

MsGoF: Breast lesion classification on ultrasound images by multi-scale gradational-order fusion framework.

作者信息

Zhong Shengzhou, Tu Chao, Dong Xiuyu, Feng Qianjin, Chen Wufan, Zhang Yu

机构信息

School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.

出版信息

Comput Methods Programs Biomed. 2023 Mar;230:107346. doi: 10.1016/j.cmpb.2023.107346. Epub 2023 Jan 19.

DOI:10.1016/j.cmpb.2023.107346

PMID:36716637

Abstract

BACKGROUND AND OBJECTIVE

Predicting the malignant potential of breast lesions based on breast ultrasound (BUS) images is a crucial component of computer-aided diagnosis system for breast cancers. However, since breast lesions in BUS images generally have various shapes with relatively low contrast and present complex textures, it still remains challenging to accurately identify the malignant potential of breast lesions.

METHODS

In this paper, we propose a multi-scale gradational-order fusion framework to make full advantages of multi-scale representations incorporating with gradational-order characteristics of BUS images for breast lesions classification. Specifically, we first construct a spatial context aggregation module to generate multi-scale context representations from the original BUS images. Subsequently, multi-scale representations are efficiently fused in feature fusion block that is armed with special fusion strategies to comprehensively capture morphological characteristics of breast lesions. To better characterize complex textures and enhance non-linear modeling capability, we further propose isotropous gradational-order feature module in the feature fusion block to learn and combine multi-order representations. Finally, these multi-scale gradational-order representations are utilized to perform prediction for the malignant potential of breast lesions.

RESULTS

The proposed model was evaluated on three open datasets by using 5-fold cross-validation. The experimental results (Accuracy: 85.32%, Sensitivity: 85.24%, Specificity: 88.57%, AUC: 90.63% on dataset A; Accuracy: 76.48%, Sensitivity: 72.45%, Specificity: 80.42%, AUC: 78.98% on dataset B) demonstrate that the proposed method achieves the promising performance when compared with other deep learning-based methods in BUS classification task.

CONCLUSIONS

The proposed method has demonstrated a promising potential to predict malignant potential of breast lesion using ultrasound image in an end-to-end manner.

摘要

背景与目的

基于乳腺超声（BUS）图像预测乳腺病变的恶性潜能是乳腺癌计算机辅助诊断系统的关键组成部分。然而，由于BUS图像中的乳腺病变通常具有各种形状，对比度相对较低且纹理复杂，准确识别乳腺病变的恶性潜能仍然具有挑战性。

方法

在本文中，我们提出了一种多尺度分级顺序融合框架，以充分利用多尺度表示，并结合BUS图像的分级顺序特征进行乳腺病变分类。具体而言，我们首先构建一个空间上下文聚合模块，从原始BUS图像生成多尺度上下文表示。随后，多尺度表示在特征融合块中进行有效融合，该融合块采用特殊的融合策略，以全面捕捉乳腺病变的形态特征。为了更好地表征复杂纹理并增强非线性建模能力，我们在特征融合块中进一步提出各向同性分级顺序特征模块，以学习和组合多阶表示。最后，利用这些多尺度分级顺序表示对乳腺病变的恶性潜能进行预测。

结果

所提出的模型通过5折交叉验证在三个公开数据集上进行了评估。实验结果（数据集A上的准确率：85.32%，灵敏度：85.24%，特异性：88.57%，AUC：90.63%；数据集B上的准确率：76.48%，灵敏度：72.45%，特异性：80.42%，AUC：78.98%）表明，与其他基于深度学习的方法相比，所提出的方法在BUS分类任务中取得了有前景的性能。