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首页> 外文期刊>Computer vision and image understanding >PS-DeVCEM: Pathology-sensitive deep learning model for video capsule endoscopy based on weakly labeled data
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PS-DeVCEM: Pathology-sensitive deep learning model for video capsule endoscopy based on weakly labeled data

机译：PS-DEVCEM：基于弱标记数据的视频胶囊内窥镜检查的病理敏感的深度学习模型

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摘要

We propose a novel pathology-sensitive deep learning model (PS-DeVCEM) for frame-level anomaly detection and multi-label classification of different colon diseases in video capsule endoscopy (VCE) data.Our proposed model is capable of coping with the key challenge of colon apparent heterogeneity caused by several types of diseases.Our model is driven by attention-based deep multiple instance learning and is trained end-to-end on weakly labeled data using video labels instead of detailed frame-by-frame annotation.This makes it a cost-effective approach for the analysis of large capsule video endoscopy repositories.Other advantages of our proposed model include its capability to localize gastrointestinal anomalies in the temporal domain within the video frames, and its generality, in the sense that abnormal frame detection is based on automatically derived image features.The spatial and temporal features are obtained through ResNet50 and residual Long short-term memory (residual LSTM) blocks, respectively.Additionally, the learned temporal attention module provides the importance of each frame to the final label prediction.Moreover, we developed a self-supervision method to maximize the distance between classes of pathologies.We demonstrate through qualitative and quantitative experiments that our proposed weakly supervised learning model gives a superior precision and Fl-score reaching, 61.6% and 55.1%, as compared to three state-of-the-art video analysis methods respectively.We also show our model's ability to temporally localize frames with pathologies, without frame annotation information during training.Furthermore, we collected and annotated the first and largest VCE dataset with only video labels.The dataset contains 455 short video segments with 28,304 frames and 14 classes of colorectal diseases and artifacts.Dataset and code supporting this publication will be made available on our home page.
机译：我们提出了一种新的病理敏感的深度学习模型（PS-DEVCEM），用于帧级异常检测和视频胶囊内窥镜（VCE）数据的不同结肠疾病的多标签分类。我们提出的模型能够应对关键挑战由若干类型疾病引起的结肠表观异质性。我们模型由基于注意的深度多实例学习驱动，并使用视频标签而不是详细的逐帧注释在弱标记的数据上训练结束于终端。这使得它是一种经济有效的方法，用于分析大胶囊视频内窥镜储存库。我们所提出的模型的其他优势包括其在异常帧检测的意义上的逐个域中的时间域中定位胃肠道异常的能力基于自动导出的图像特征。通过Reset50和剩余的长短期内存（残留LSTM）BL获得空间和时间特征分别参加了学习的时间注意力模块向最终标签预测提供每个帧的重要性。我们开发了一种自我监督方法，以最大化病例类别的距离。我们通过定性和定量实验证明了我们的定性和定量实验拟议的弱监督学习模型具有优异的精度和飞行率达到61.6％和55.1％，与三种最先进的视频分析方法相比。我们还表明我们的模型能够在暂时将框架与病理暂时定位框架，如果没有帧训练期间的帧注释信息，我们将收集并注释仅具有视频标签的第一个和最大的VCE数据集。数据集包含455个短视频段，具有28,304帧和14级结肠直肠疾病和Artifacts.dataset和代码支持此出版物的代码在我们的主页上提供。

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