###### Abstract  
Deep learning demands a large amount of annotated data, and the annotation task is often crowdsourced for economic efficiency. When the annotation task is delegated to non-experts, the dataset may contain data with inaccurate labels. Noisy labels not only yield classification models with sub-optimal performance, but may also impede their optimization dynamics. In this work, we propose exploiting the pattern recognition capacity of deep convolutional neural networks to filter out supposedly mislabeled cases while training. We suggest a training method that references softmax outputs to judge the correctness of the given labels. This approach achieved outstanding performance compared to the existing methods in various noise settings on a large-scale dataset ([PERSON] 2015 Diabetic Retinopathy). Furthermore, we demonstrate a method mining positive cases from a pool of unlabeled images by exploiting the generalization ability. With this method, we won first place on the offsite validation dataset in pathological myopia classification challenge (PALM), achieving the AUROC of 0.9993 in the final submission. Source codes are publicly available.  
diabetic retinopathy; pathologic myopia; classification; retinal image analysis; fundus image; deep convolutional neural network; Semi-supervised learning +
Footnote †: journal: Medical  
of CNNs to tackle label noises. In co-teaching [26], two networks are used to select data with small losses to train the other network. This idea was motivated by the fact that data with small losses are likely to be correct. However, ordering data based on their resulting loss overlooks difficult cases that may be correctly labeled but simply hard to train. A network trained using co-teaching may ultimately fail to perform reliably on hard examples and human intervention may be necessary to separate hard examples from those with incorrect labels [27]. Another approach optimizes network weights and replaces labels with the predictions intermittently [28]. The method exploits the generalization ability of the network and performs reliably in highly noisy data. However, this method also does not differentiate correctly labeled hard examples from incorrectly labeled data.  
In this paper, we discuss two methods that utilize the generalization ability of CNNs to mitigate the problem of inconsistency or scarcity of medical data annotation. First, we suggest a method to filter incorrectly labeled data based on discrepancies between the network's logits and given labels. We applied this approach to the classification of referable diabetic retinopathy from color fundus photographs and showed that the proposed algorithm outperforms state-of-the-art methods. Secondly, we introduce an active learning algorithm to mine rare positive cases from a set of unlabeled images. A model trained using this method ranked first on the offsite validation dataset as part of the PALM challenge, achieving an AUROC of 0.9993 in the final submission (ranking and performance of individual teams can be seen in i-Challenge PM tab at [[http://ai.baidu.com/broad/leaderboard](http://ai.baidu.com/broad/leaderboard)]([http://ai.baidu.com/broad/leaderboard](http://ai.baidu.com/broad/leaderboard))). Source codes are available at [[https://bitbucket.org/waalsdnd/codes_and_data](https://bitbucket.org/waalsdnd/codes_and_data)]([https://bitbucket.org/waalsdnd/codes_and_data](https://bitbucket.org/waalsdnd/codes_and_data)).