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Pseudo-Labeling for Small Lesion Detection on Diabetic Retinopathy Images

机译：用于糖尿病性视网膜病变图像的小病变检测的伪标记

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Diabetic retinopathy (DR) is a primary cause of blindness in working-age people worldwide. About 3 to 4 million people with diabetes become blind because of DR every year. Diagnosis of DR through color fundus images is a common approach to mitigate such problem. However, DR diagnosis is a difficult and time consuming task, which requires experienced clinicians to identify the presence and significance of many small features on high resolution images. Convolutional Neural Network (CNN) has proved to be a promising approach for automatic biomedical image analysis recently. In this work, we investigate lesion detection on DR fundus images with CNN-based object detection methods. Lesion detection on fundus images faces two unique challenges. The first one is that our dataset is not fully labeled, i.e., only a subset of all lesion instances are marked. Not only will these unlabeled lesion instances not contribute to the training of the model, but also they will be mistakenly counted as false negatives, leading the model move to the opposite direction. The second challenge is that the lesion instances are usually very small, making them difficult to be found by normal object detectors. To address the first challenge, we introduce an iterative training algorithm for the semi-supervised method of pseudo-labeling, in which a considerable number of unlabeled lesion instances can be discovered to boost the performance of the lesion detector. For the small size targets problem, we extend both the input size and the depth of feature pyramid network (FPN) to produce a large CNN feature map, which can preserve the detail of small lesions and thus enhance the effectiveness of the lesion detector. The experimental results show that our proposed methods significantly outperform the baselines.
机译：糖尿病性视网膜病（DR）是全世界工作年龄人群失明的主要原因。每年约有3到4百万的糖尿病患者因DR失明。通过彩色眼底图像诊断DR是缓解此类问题的常用方法。但是，DR诊断是一项艰巨且耗时的任务，需要经验丰富的临床医生来识别高分辨率图像上许多小特征的存在和重要性。最近，卷积神经网络（CNN）已被证明是一种有前途的自动生物医学图像分析方法。在这项工作中，我们调查了基于CNN的对象检测方法对DR眼底图像的病变检测。眼底图像的病变检测面临两个独特的挑战。第一个是我们的数据集未完全标记，即仅标记了所有病变实例的一个子集。这些未标记的病变实例不仅不会有助于模型的训练，而且会被误认为是假阴性，从而导致模型朝相反的方向发展。第二个挑战是病变实例通常很小，这使得普通物体检测器很难发现它们。为了解决第一个挑战，我们为伪标记的半监督方法引入了一种迭代训练算法，其中可以发现大量未标记的病变实例，以提高病变检测器的性能。对于小尺寸目标问题，我们扩展了输入尺寸和特征金字塔网络（FPN）的深度，以生成大型的CNN特征图，该图可以保留小病变的细节，从而提高病变检测器的有效性。实验结果表明，我们提出的方法明显优于基线。

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