A collaborative multi-task model for immunohistochemical molecular sub-types of multi-modal breast cancer MRI images

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control Pub Date : 2024-11-08 DOI:10.1016/j.bspc.2024.107137

Haozhen Xiang , Yuqi Xiong , Yingwei Shen , Jiaxin Li , Deshan Liu

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引用次数: 0

Abstract

Clinically, personalized treatment developed based on the immunohistochemical (IHC) molecular sub-types of breast cancer can enhance long-term survival rates. Nevertheless, IHC, as an invasive detection method, may pose some risks of tumor metastasis caused by puncture. This work propose a collaborative multi-task model based on multi-modal data. Firstly, a dual-stream learning network based on Swin Transformer is employed to extract features from both DCE and T1WI images. Specifically, an Shared Representation (SR) module extracts shared representations, while an Enhancement of Unique features (EU) module enhances specific features. Subsequently, a multi-path classification network is constructed, which comprehensively considers the MRI image features, lesion location, and morphological features. Comprehensive experiments using clinical MRI images show the proposed method outperforms state-of-the-art, with an accuracy of 85.1%, sensitivity of 84.0%, specificity of 95.1%, and an F1 score of 83.6%.

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多模态乳腺癌 MRI 图像免疫组化分子亚型的多任务协作模型

在临床上，根据乳腺癌的免疫组化（IHC）分子亚型进行个性化治疗可以提高长期生存率。然而，IHC 作为一种侵入性检测方法，可能存在穿刺导致肿瘤转移的风险。本研究提出了一种基于多模态数据的多任务协作模型。首先，采用基于 Swin Transformer 的双流学习网络从 DCE 和 T1WI 图像中提取特征。具体来说，共享表征（SR）模块提取共享表征，而增强独特特征（EU）模块则增强特定特征。随后，构建了一个多路径分类网络，该网络综合考虑了磁共振成像特征、病变位置和形态特征。使用临床 MRI 图像进行的综合实验表明，所提出的方法优于最先进的方法，准确率为 85.1%，灵敏度为 84.0%，特异性为 95.1%，F1 得分为 83.6%。

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来源期刊

Biomedical Signal Processing and Control 工程技术-工程：生物医学

CiteScore

9.80

自引率

13.70%

发文量

822

审稿时长

4 months

期刊介绍： Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.