Multi-scale Lesion Feature Fusion and Location-Aware for Chest Multi-disease Detection.
Accurately identifying and locating lesions in chest X-rays has the potential to significantly enhance diagnostic efficiency, quality, and interpretability. However, current methods primarily focus on detecting of specific diseases in chest X-rays, disregarding the presence of multiple diseases in a...
| Published in: | Journal of Digital Imaging Vol. 37; no. 6; pp. 2752 - 2768 |
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| Main Authors: | , , , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Dec2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182283962&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182283962 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2024 vid: 37 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 182283962 182283962 182283962 10.1007/s10278-024-01133-7 182283962 ppf: 2752 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multi-scale Lesion Feature Fusion and Location-Aware for Chest Multi-disease Detection. aug: au: Yuan, Yubo Liu, Lijun Yang, Xiaobing Liu, Li Huang, Qingsong affil: https://ror.org/00xyeez13 School of Information Engineering and Automation, Kunming University of Science and Technology, 650500, Kunming, China sug: subj: Radiography, Thoracic Diagnostic Imaging Thoracic Diseases Diagnosis Models, Theoretical Funding Source Human Convolutional Neural Networks Semantics Conceptual Framework Radiographic Image Enhancement Descriptive Statistics ab: Accurately identifying and locating lesions in chest X-rays has the potential to significantly enhance diagnostic efficiency, quality, and interpretability. However, current methods primarily focus on detecting of specific diseases in chest X-rays, disregarding the presence of multiple diseases in a single chest X-ray scan. Moreover, the diversity in lesion locations and attributes introduces complexity in accurately discerning specific traits for each lesion, leading to diminished accuracy when detecting multiple diseases. To address these issues, we propose a novel detection framework that enhances multi-scale lesion feature extraction and fusion, improving lesion position perception and subsequently boosting chest multi-disease detection performance. Initially, we construct a multi-scale lesion feature extraction network to tackle the uniqueness of various lesion features and locations, strengthening the global semantic correlation between lesion features and their positions. Following this, we introduce an instance-aware semantic enhancement network that dynamically amalgamates instance-specific features with high-level semantic representations across various scales. This adaptive integration effectively mitigates the loss of detailed information within lesion regions. Additionally, we perform lesion region feature mapping using candidate boxes to preserve crucial positional information, enhancing the accuracy of chest disease detection across multiple scales. Experimental results on the VinDr-CXR dataset reveal a 6% increment in mean average precision (mAP) and an 8.4% improvement in mean recall (mR) when compared to state-of-the-art baselines. This demonstrates the effectiveness of the model in accurately detecting multiple chest diseases by capturing specific features and location information. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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