论文标题
针对弱监督疾病定位和分类的显着性图改进的区域建议
Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification
论文作者
论文摘要
自动化系统的部署以从医学图像诊断疾病的情况下，挑战了将诊断疾病定位以证明或解释分类决定的诊断疾病的要求。这一要求很难满足，因为可以开发这些系统的大多数培训集仅包含全球注释，从而使疾病的本地化成为弱监督的方法。专为弱监督的疾病分类和定位而设计的主要方法依赖于未针对本地化训练的显着性或注意力图，或者是无法完善以产生准确检测的区域建议。在本文中，我们引入了一种新模型，该模型结合了区域建议和显着性检测，以克服弱监督疾病分类和定位的这两个局限性。使用ChestX-Ray14数据集，我们表明我们提出的模型为弱监督疾病的诊断和定位建立了新的最新技术。
The deployment of automated systems to diagnose diseases from medical images is challenged by the requirement to localise the diagnosed diseases to justify or explain the classification decision. This requirement is hard to fulfil because most of the training sets available to develop these systems only contain global annotations, making the localisation of diseases a weakly supervised approach. The main methods designed for weakly supervised disease classification and localisation rely on saliency or attention maps that are not specifically trained for localisation, or on region proposals that can not be refined to produce accurate detections. In this paper, we introduce a new model that combines region proposal and saliency detection to overcome both limitations for weakly supervised disease classification and localisation. Using the ChestX-ray14 data set, we show that our proposed model establishes the new state-of-the-art for weakly-supervised disease diagnosis and localisation.
