{"title":"通过动态多任务学习进行无线胶囊内窥镜异常分类","authors":"Xingcun Li , Qinghua Wu , Kun Wu","doi":"10.1016/j.bspc.2024.107081","DOIUrl":null,"url":null,"abstract":"<div><div>Wireless capsule endoscopy (WCE) provides a painless, non-invasive means for early gastrointestinal disease detection and cancer prevention. However, clinicians must diagnose only about 5% of lesion images from tens of thousands of frames, highlighting the need for computer-assisted diagnostic methods to enhance efficiency and reduce the elevated misdiagnosis rates attributed to visual fatigue. Previous research heavily relied on module design, an effective yet highly coupled method with the baseline and incurring additional computational costs. This paper proposes a dynamic multi-task learning method that combines triplet loss and weighted cross-entropy loss to respectively guide the model in learning compact fine-grained representations and establishing less biased decision boundaries, without incurring additional computational costs. Our method outperforms previous advanced methods on two publicly available datasets, achieving an F1 score of 96.47% on Kvasir-Capsule and an F1 score of 96.75% with an accuracy of 96.72% on CAD-CAP. Visualization of the representations and heatmaps confirms the model’s precision in focusing on the lesion area. The prediction model has been uploaded to <span><span>https://github.com/xli122/WCE_MTL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Wireless capsule endoscopy anomaly classification via dynamic multi-task learning\",\"authors\":\"Xingcun Li , Qinghua Wu , Kun Wu\",\"doi\":\"10.1016/j.bspc.2024.107081\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Wireless capsule endoscopy (WCE) provides a painless, non-invasive means for early gastrointestinal disease detection and cancer prevention. However, clinicians must diagnose only about 5% of lesion images from tens of thousands of frames, highlighting the need for computer-assisted diagnostic methods to enhance efficiency and reduce the elevated misdiagnosis rates attributed to visual fatigue. Previous research heavily relied on module design, an effective yet highly coupled method with the baseline and incurring additional computational costs. This paper proposes a dynamic multi-task learning method that combines triplet loss and weighted cross-entropy loss to respectively guide the model in learning compact fine-grained representations and establishing less biased decision boundaries, without incurring additional computational costs. Our method outperforms previous advanced methods on two publicly available datasets, achieving an F1 score of 96.47% on Kvasir-Capsule and an F1 score of 96.75% with an accuracy of 96.72% on CAD-CAP. Visualization of the representations and heatmaps confirms the model’s precision in focusing on the lesion area. The prediction model has been uploaded to <span><span>https://github.com/xli122/WCE_MTL</span><svg><path></path></svg></span>.</div></div>\",\"PeriodicalId\":55362,\"journal\":{\"name\":\"Biomedical Signal Processing and Control\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical Signal Processing and Control\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S174680942401139X\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Signal Processing and Control","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S174680942401139X","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
摘要
无线胶囊内窥镜(WCE)为早期胃肠道疾病检测和癌症预防提供了一种无痛、无创的手段。然而,临床医生只能从数万帧病变图像中诊断出约 5%的病变,这凸显了对计算机辅助诊断方法的需求,以提高效率并降低因视觉疲劳而导致的高误诊率。以往的研究在很大程度上依赖于模块设计,这是一种有效但与基线高度耦合的方法,会产生额外的计算成本。本文提出了一种动态多任务学习方法,该方法结合了三重损失和加权交叉熵损失,分别指导模型学习紧凑的细粒度表征和建立较少偏差的决策边界,同时不会产生额外的计算成本。我们的方法在两个公开数据集上的表现优于之前的先进方法,在 Kvasir-Capsule 数据集上的 F1 得分为 96.47%,在 CAD-CAP 数据集上的 F1 得分为 96.75%,准确率为 96.72%。可视化表示和热图证实了该模型在聚焦病变区域方面的精确性。预测模型已上传至 https://github.com/xli122/WCE_MTL。
Wireless capsule endoscopy anomaly classification via dynamic multi-task learning
Wireless capsule endoscopy (WCE) provides a painless, non-invasive means for early gastrointestinal disease detection and cancer prevention. However, clinicians must diagnose only about 5% of lesion images from tens of thousands of frames, highlighting the need for computer-assisted diagnostic methods to enhance efficiency and reduce the elevated misdiagnosis rates attributed to visual fatigue. Previous research heavily relied on module design, an effective yet highly coupled method with the baseline and incurring additional computational costs. This paper proposes a dynamic multi-task learning method that combines triplet loss and weighted cross-entropy loss to respectively guide the model in learning compact fine-grained representations and establishing less biased decision boundaries, without incurring additional computational costs. Our method outperforms previous advanced methods on two publicly available datasets, achieving an F1 score of 96.47% on Kvasir-Capsule and an F1 score of 96.75% with an accuracy of 96.72% on CAD-CAP. Visualization of the representations and heatmaps confirms the model’s precision in focusing on the lesion area. The prediction model has been uploaded to https://github.com/xli122/WCE_MTL.
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.