A lightweight spatially-aware classification model for breast cancer pathology images

IF 5.3 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Liang Jiang, Cheng Zhang, Huan Zhang, Hui Cao
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引用次数: 0

Abstract

Breast cancer is a prevalent malignant tumour with high global incidence. Its diagnosis relies primarily on the analysis of pathological breast images. Owing to the complex organisation of the tumour microenvironment, neural network models are essential as efficient classification tools in the field of pathological image analysis. This study introduced spatially-aware attention swift parallel convolution network (SPA-SPCNet), a lightweight and low-latency model for classifying breast pathologies. A novel module for multi-scale feature extraction was constructed using a depthwise separable convolution method. It focuses on the multi-scale features of pathological images to alleviate recognition problems caused by similar local features in breast cancer tissues. The module concatenates the convolutions of different kernels from three branches. Second, a lightweight dynamic spatially-aware attention module was introduced to integrate the visual graph convolutional architecture in a branch. This allowed the model to capture the spatial structure and relationships in image, enabling better handling of the unique spatial distribution relationship between breast cancer tissue structures. The other branch utilises a self-attention mechanism in the transformer. The module can dynamically adjust the attention of the model to different regions in the image, allowing it to focus on the key features of the complex spatial distribution of breast cancer tissue. This feature fusion method enabled the model to capture both global semantics and local details. Compared with existing lightweight models, the proposed model has advantages in terms of tissue structure classification accuracy, parameter quantity, floating-point operations, and real-time inference speed, providing a powerful tool for computer-aided breast pathological image classification.

乳腺癌病理图像的轻量级空间感知分类模型
乳腺癌是一种常见的恶性肿瘤,全球发病率很高。其诊断主要依靠对乳腺病理图像的分析。由于肿瘤微环境的复杂组织结构,神经网络模型是病理图像分析领域必不可少的高效分类工具。本研究引入了空间感知注意力敏捷并行卷积网络(SPA-SPCNet),这是一种轻量级、低延迟的乳腺病理分类模型。利用深度可分离卷积法构建了一个用于多尺度特征提取的新模块。它侧重于病理图像的多尺度特征,以缓解乳腺癌组织中相似局部特征所造成的识别问题。该模块将三个分支的不同核卷积合并在一起。其次,引入了轻量级动态空间感知注意力模块,将视觉图卷积架构整合到一个分支中。这使得模型能够捕捉图像中的空间结构和关系,从而更好地处理乳腺癌组织结构之间独特的空间分布关系。另一个分支利用了变压器中的自注意机制。该模块可动态调整模型对图像中不同区域的关注度,使其关注乳腺癌组织复杂空间分布的关键特征。这种特征融合方法使模型既能捕捉全局语义,又能捕捉局部细节。与现有的轻量级模型相比,所提出的模型在组织结构分类精度、参数数量、浮点运算和实时推理速度等方面都具有优势,为计算机辅助乳腺病理图像分类提供了强有力的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
16.50
自引率
6.20%
发文量
77
审稿时长
38 days
期刊介绍: Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.
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