奥斯卡:一个异常值敏感的基于内容的放射图像检索系统

Xiaoyuan Guo, Jiali Duan, S. Purkayastha, H. Trivedi, J. Gichoya, I. Banerjee
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摘要

提高对噪声数据集的检索相关性是医学领域大规模干净数据集管理的一个新兴需求。虽然现有的方法可以应用于类检索(即。类间),它们不能区分同一类(又名。内部类)。在医疗外部数据集上,这个问题更加严重,在训练过程中,同一类别的噪声样本被平等对待。我们的目标是为细粒度检索识别类内/类间相似性。为了实现这一目标,我们提出了一个基于离群值敏感内容的放射学检索系统(OSCARS),包括两个步骤。首先,我们以无监督的方式在一个干净的内部数据集上训练一个离群值检测器。然后,我们使用训练好的检测器在外部数据集上生成异常分数,其分布将用于抑制类内变化。其次,我们提出了一种四组(a, p, nintra, ninter)抽样策略,其中类内负的nintra从与a所属的bin锚点不同的同一类的bin中抽样,而n_inter则从类间随机抽样。我们提出了一个加权度量学习目标来平衡类内和类间的特征学习。我们在两个具有代表性的公共放射照相数据集上进行了实验。实验证明了该方法的有效性。培训和评估代码可以在https://github.com/XiaoyuanGuo/oscars找到。
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OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval System
Improving the retrieval relevance on noisy datasets is an emerging need for the curation of a large-scale clean dataset in the medical domain. While existing methods can be applied for class-wise retrieval (aka. inter-class), they cannot distinguish the granularity of likeness within the same class (aka. intra-class). The problem is exacerbated on medical external datasets, where noisy samples of the same class are treated equally during training. Our goal is to identify both intra/inter-class similarities for fine-grained retrieval. To achieve this, we propose an Outlier-Sensitive Content-based rAdiologhy Retrieval System (OSCARS), consisting of two steps. First, we train an outlier detector on a clean internal dataset in an unsupervised manner. Then we use the trained detector to generate the anomaly scores on the external dataset, whose distribution will be used to bin intra-class variations. Second, we propose a quadruplet (a, p, nintra, ninter) sampling strategy, where intra-class negatives nintra are sampled from bins of the same class other than the bin anchor a belongs to, while n_inter are randomly sampled from inter-classes. We suggest a weighted metric learning objective to balance the intra and inter-class feature learning. We experimented on two representative public radiography datasets. Experiments show the effectiveness of our approach. The training and evaluation code can be found in https://github.com/XiaoyuanGuo/oscars.
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