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首页> 外文会议>International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging;International Workshop on Clinical Image-Based Procedures;International Conference on Medical Image Computing and Computer Assisted Intervention >Reg R-CNN: Lesion Detection and Grading Under Noisy Labels
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Reg R-CNN: Lesion Detection and Grading Under Noisy Labels

机译：Reg R-CNN：嘈杂标签下的病变检测和分级

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摘要

For the task of concurrently detecting and categorizing objects, the medical imaging community commonly adopts methods developed on natural images. Current state-of-the-art object detectors are comprised of two stages: the first stage generates region proposals, the second stage subsequently categorizes them. Unlike in natural images, however, for anatomical structures of interest such as tumors, the appearance in the image (e.g., scale or intensity) links to a malignancy grade that lies on a continuous ordinal scale. While classification models discard this ordinal relation between grades by discretizing the continuous scale to an unordered bag of categories, regression models are trained with distance metrics, which preserve the relation. This advantage becomes all the more important in the setting of label confusions on ambiguous data sets, which is the usual case with medical images. To this end, we propose Reg R-CNN, which replaces the second-stage classification model of a current object detector with a regression model. We show the superiority of our approach on a public data set with 1026 patients and a series of toy experiments. Code will be available at github.com/MIC-DKFZ/RegRCNN.
机译：为了同时检测和分类物体，医学影像界通常采用在自然影像上开发的方法。当前最新的物体检测器包括两个阶段：第一阶段生成区域提议，第二阶段随后将它们分类。但是，与自然图像不同，对于感兴趣的解剖结构，例如肿瘤，图像中的外观（例如，鳞片或强度）与处于连续序数尺度上的恶性等级有关。尽管分类模型通过将连续量表离散化为无序的类别包而放弃了等级之间的这种序数关系，但是回归模型是使用距离度量来训练的，从而保留了这种关系。在模糊数据集上设置标签混淆时，这一优势变得尤为重要，这在医学图像中很常见。为此，我们提出了Reg R-CNN，它用回归模型代替了当前目标检测器的第二阶段分类模型。我们在1026名患者的公共数据集和一系列玩具实验中展示了我们方法的优越性。代码将在github.com/MIC-DKFZ/RegRCNN上提供。

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