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Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem.
IEEE Transactions on Medical Imaging ( IF 10.6 ) Pub Date : 2019-08-19 , DOI: 10.1109/tmi.2019.2936244
Ruwan Tennakoon , Gerda Bortsova , Silas Orting , Amirali K. Gostar , Mathilde M. W. Wille , Zaigham Saghir , Reza Hoseinnezhad , Marleen de Bruijne , Alireza Bab-Hadiashar

Volumetric imaging is an essential diagnostic tool for medical practitioners. The use of popular techniques such as convolutional neural networks (CNN) for analysis of volumetric images is constrained by the availability of detailed (with local annotations) training data and GPU memory. In this paper, the volumetric image classification problem is posed as a multiinstance classification problem and a novel method is proposed to adaptively select positive instances from positive bags during the training phase. This method uses the extreme value theory to model the feature distribution of the images without a pathology and use it to identify positive instances of an imaged pathology. The experimental results, on three separate image classification tasks (i.e. classify retinal OCT images according to the presence or absence of fluid build-ups, emphysema detection in pulmonary 3D-CT images and detection of cancerous regions in 2D histopathology images) show that the proposed method produces classifiers that have similar performance to fully supervised methods and achieves the state of the art performance in all examined test cases.

中文翻译:

使用多实例学习和极值定理对体积图像进行分类。

体积成像是医学从业人员必不可少的诊断工具。卷积神经网络(CNN)等流行技术在体积图像分析中的使用受到详细(带有局部注释)训练数据和GPU内存可用性的限制。本文将体图像分类问题作为一个多实例分类问题,提出了一种在训练阶段从阳性袋中自适应选择阳性实例的新方法。该方法使用极值理论来对没有病理学的图像特征分布进行建模,并使用它来识别成像病理学的阳性实例。在三个单独的图像分类任务上(即根据是否存在积液对视网膜OCT图像进行分类)的实验结果,
更新日期:2020-04-22
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