基于多尺度注意力信息复用网络的胸片图像分类

doi:10.13229/j.cnki.jdxbgxb.20240222

Abstract

Abstract:

To address issues such as low recognition of lesion areas in chest X-ray images and the difficulty in accurately capturing the spatial positions of lesions， a multi-scale attention information multiplexing network that helps improve the dassification accuracy of chest X-ray images was proposed in this paper. Firstly， by introducing multiple spatial information multiplexing blocks， the network enhances the positional connections between disease regions on feature maps and across channels； Secondly， through a multi-scale integration attention blocks， the network integrates multi-scale image feature information to automatically capture disease location variations and flexibly focus on key pathological information； Finally， the problem of imbalanced distribution of chest disease samples was alleviated by using an asymmetric shift focus loss function. Multiple experiments on the publicly available datasets ChestX-ray14 and CheXpert have shown that the average area under curve （AUC） value of the proposed network on two datasets reached 0.847 and 0.901 respectively， which is superior the more advanced network models in recent years. This indicates that the network can effectively improve the classification accuracy of chest diseases.

Key words: computer application technology, chest X-ray image classification, spatial information multiplexing, multi-scale attention, asymmetric shift focus loss function

CLC Number:

TP391

Rui-feng ZHANG,Fang-zhao GUO,Qiang LI. Chest X-ray images classification based on multi-scale attention information multiplexing network[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(11): 3686-3696.

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References 21

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疾病种类	阳性	不确定	阴性
肺不张	29 333	29 377	165 606
心脏肿大	23 002	6 597	194 717
肺实变	12 730	23 976	187 610
水肿	48 905	11 571	163 840
胸腔积液	75 696	9 419	139 201

疾病种类	DCNN	ConsultNet	Deformab-CDAM-D	A³Net	PCSANet	CheXGAT	PCAN	SSGE	MIM-Net
平均AUC	0.745	0.822	0.840	0.826	0.825	0.827	0.824	0.830	0.847
肺不张	0.700	0.785	0.820	0.779	0.807	0.787	0.785	0.792	0.826
心脏肿大	0.810	0.899	0.912	0.895	0.910	0.879	0.897	0.892	0.919
积液	0.759	0.835	0.890	0.836	0.879	0.837	0.837	0.840	0.887
渗透	0.661	0.699	0.714	0.710	0.698	0.699	0.706	0.714	0.715
肿块	0.693	0.838	0.865	0.834	0.824	0.839	0.834	0.848	0.869
肺结节	0.669	0.775	0.772	0.777	0.750	0.793	0.786	0.812	0.784
肺炎	0.658	0.738	0.762	0.737	0.750	0.741	0.730	0.733	0.801
气胸	0.799	0.871	0.903	0.878	0.850	0.879	0.871	0.885	0.892
肺实变	0.703	0.763	0.810	0.759	0.802	0.755	0.763	0.753	0.809
水肿	0.805	0.850	0.896	0.855	0.888	0.851	0.854	0.848	0.900
肺气肿	0.833	0.924	0.914	0.933	0.890	0.945	0.921	0.948	0.924
纤维化	0.786	0.831	0.808	0.838	0.812	0.842	0.817	0.827	0.824
胸膜增厚	0.684	0.776	0.815	0.791	0.768	0.794	0.791	0.795	0.822
疝气	0.872	0.922	0.876	0.938	0.915	0.931	0.943	0.932	0.889

疾病类型	Ensemble （U-Ones）	ConsultNet （U-Ones）	PCAN （U-Ones）	MIM-Net （U-Ones）	Ensemble （U-Zeros）	ConsultNet （U-Zeros）	DCNN （U-Zeros）	MIM-Net （U-Zeros）
肺不张	0.858	0.847	0.848	0.859	0.811	0.804	0.745	0.842
心脏肿大	0.832	0.868	0.865	0.885	0.840	0.874	0.813	0.873
肺实变	0.899	0.923	0.908	0.904	0.932	0.940	0.882	0.901
水肿	0.941	0.924	0.912	0.935	0.929	0.894	0.921	0.928
积液	0.934	0.926	0.940	0.924	0.931	0.923	0.930	0.923
平均值	0.893	0.898	0.895	0.901	0.889	0.889	0.858	0.893

MSIM	×	×	√	√
MIA	×	√	×	√
肺不张	0.822	0.811	0.824	0.826
心脏肿大	0.911	0.907	0.918	0.919
积液	0.877	0.892	0.885	0.887
渗透	0.714	0.718	0.715	0.714
肿块	0.865	0.858	0.866	0.869
肺结节	0.779	0.791	0.786	0.784
肺炎	0.765	0.771	0.799	0.801
气胸	0.852	0.879	0.876	0.892
肺实变	0.817	0.801	0.805	0.809
水肿	0.895	0.891	0.897	0.900
肺气肿	0.917	0.915	0.926	0.924
纤维化	0.803	0.817	0.814	0.824
胸膜增厚	0.819	0.808	0.823	0.822
疝气	0.892	0.885	0.881	0.889
平均AUC	0.838	0.839	0.844	0.847

损失函数	平均AUC
损失函数	ChestX-ray14	CheXpert
交叉熵损失函数	0.838	0.889
焦点损失函数	0.843	0.893
非对称移位焦点损失函数	0.847	0.901

Chest X-ray images classification based on multi-scale attention information multiplexing network

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