.png){kind=icon}
返回
Wireless capsule endoscopy anomaly classification via dynamic multi-task learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.bspc.2024.107081.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Deep learning
Multi-task learning
Computer-aided diagnosis
Image classification
Gastrointestinal disease
期刊
IF:
4.9
论文数:
1.0W
被引数:
2.4W
机构
暂无机构信息
引用论文
Semi-supervised WCE image classification with adaptive aggregated attention.png)
MEDICAL IMAGE ANALYSIS
MEDICAL IMAGE ANALYSISIF11.8
PREAI
分享收藏Randomized controlled trial on the skin toxicity of panitumumab in Japanese patients with metastatic colorectal cancer: HGCSG1001 study; J-STEPPFUTURE ONCOLOGY
FUTURE ONCOLOGYIF2.6
PREAI
分享收藏Synthesis of n-type semiconducting diamond film using diphosphorus pentaoxide as the doping source以五氧化二磷为掺杂源合成n型半导体金刚石膜
PREAI
分享收藏Detection of abnormality in wireless capsule endoscopy images using fractal features基于分形特征的无线胶囊内镜图像异常检测
PREAI
分享收藏
查看更多内容