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Deep learning based feature representation for automated skin histopathological image annotation

机译：基于深度学习的特征表示可自动进行皮肤组织病理学图像注释

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摘要

Automated annotation of skin biopsy histopathological images provides valuable information and supports for diagnosis, especially for the discrimination between malignant and benign lesions. Currently, computer-aid analysis of skin biopsy images mostly relied on some human-designed features, which requires expensive human efforts and experiences in problem domains. In this study, we propose an annotation framework for automated skin biopsy image analysis which makes use of a deep model for image feature representation. A convolutional neural network (CNN) is designed for local regions of skin biopsy images which learns potential high-level features automatically from input raw pixels. The annotation model is constructed in the multiple-instance multiple-label (MIML) learning framework with the features learned through the network. We achieve significant improvement of the model performance on a real world clinical skin biopsy image dataset and a benchmark dataset. Moreover, our study indicates that deep learning based model could achieve better performance than human designed features.
机译：皮肤活检组织病理学图像的自动注释可提供有价值的信息并支持诊断，尤其是对恶性和良性病变的区分。当前，皮肤活检图像的计算机辅助分析主要依赖于某些人为设计的功能，这需要昂贵的人力和在问题领域的经验。在这项研究中，我们提出了一种用于自动皮肤活检图像分析的注释框架，该框架利用了用于图像特征表示的深层模型。卷积神经网络（CNN）专为皮肤活检图像的局部区域而设计，可从输入的原始像素中自动学习潜在的高级特征。注释模型是在具有通过网络学习的功能的多实例多标签（MIML）学习框架中构建的。我们在现实世界中的临床皮肤活检图像数据集和基准数据集上实现了模型性能的显着改善。此外，我们的研究表明，基于深度学习的模型可能比人工设计的功能具有更好的性能。

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