Improving Laryngoscopy Image Analysis Through Integration of Global Information and Local Features in VoFoCD Dataset.
The diagnosis and treatment of vocal fold disorders heavily rely on the use of laryngoscopy. A comprehensive vocal fold diagnosis requires accurate identification of crucial anatomical structures and potential lesions during laryngoscopy observation. However, existing approaches have yet to explore...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 6; pp. 2794 - 2810 |
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| Autores principales: | , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182283950&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182283950 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: 182283950 182283950 182283950 10.1007/s10278-024-01068-z 182283950 ppf: 2794 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Improving Laryngoscopy Image Analysis Through Integration of Global Information and Local Features in VoFoCD Dataset. aug: au: Dao, Thao Thi Phuong Huynh, Tuan-Luc Pham, Minh-Khoi Le, Trung-Nghia Nguyen, Tan-Cong Nguyen, Quang-Thuc Tran, Bich Anh Van, Boi Ngoc Ha, Chanh Cong Tran, Minh-Triet affil: University of Science, Ho Chi Minh City, Vietnam sug: subj: Vocal Cords Pathology Laryngoscopy Diagnosis, Otorhinolaryngologic Detection Algorithms Image Interpretation, Computer Assisted Image Enhancement Decision Support Systems, Clinical Human Outpatients Inpatients Vietnam Funding Source Retrospective Design Record Review Vocal Cords Anatomy and Histology Classification Algorithms Cysts Polyps Papilloma Hyperplasia Keratosis Dislocations Neoplasms Carcinoma in Situ Carcinoma, Squamous Cell Convolutional Neural Networks ab: The diagnosis and treatment of vocal fold disorders heavily rely on the use of laryngoscopy. A comprehensive vocal fold diagnosis requires accurate identification of crucial anatomical structures and potential lesions during laryngoscopy observation. However, existing approaches have yet to explore the joint optimization of the decision-making process, including object detection and image classification tasks simultaneously. In this study, we provide a new dataset, VoFoCD, with 1724 laryngology images designed explicitly for object detection and image classification in laryngoscopy images. Images in the VoFoCD dataset are categorized into four classes and comprise six glottic object types. Moreover, we propose a novel Multitask Efficient trAnsformer network for Laryngoscopy (MEAL) to classify vocal fold images and detect glottic landmarks and lesions. To further facilitate interpretability for clinicians, MEAL provides attention maps to visualize important learned regions for explainable artificial intelligence results toward supporting clinical decision-making. We also analyze our model's effectiveness in simulated clinical scenarios where shaking of the laryngoscopy process occurs. The proposed model demonstrates outstanding performance on our VoFoCD dataset. The accuracy for image classification and mean average precision at an intersection over a union threshold of 0.5 (mAP50) for object detection are 0.951 and 0.874, respectively. Our MEAL method integrates global knowledge, encompassing general laryngoscopy image classification, into local features, which refer to distinct anatomical regions of the vocal fold, particularly abnormal regions, including benign and malignant lesions. Our contribution can effectively aid laryngologists in identifying benign or malignant lesions of vocal folds and classifying images in the laryngeal endoscopy process visually. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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