Computer Science and Engineering, Northeastern University, Shenyang, China.
Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China.
Comput Med Imaging Graph. 2018 Nov;69:112-124. doi: 10.1016/j.compmedimag.2018.08.008. Epub 2018 Aug 25.
Diabetic retinopathy (DR) is one of the most serious complications of diabetes. Early detection and treatment of DR are key public health interventions that can significantly reduce the risk of vision loss. How to effectively screen and diagnose the retinal fundus image in order to identify retinopathy in time is a major challenge. In the traditional DR screening system, the accuracy of micro-aneurysm (MA) and hemorrhagic (H) lesion detection determines the final screening performance. The detection method produced a large number of false positive samples for guaranteeing high sensitivity, and the classification model was not effective in removing false positives since the suspicious lesions lack label information.
In order to solve the problem of supervised learning in the diagnosis of DR, we formulate weakly supervised multi-class DR grading as a multi-class multi-instance problem where each image (bag) is labeled as healthy or abnormal and consists of unlabeled candidate lesion regions (instances). Specifically, we proposed a multi-kernel multi-instance learning method based on graph kernel. Moreover, we develop an under-sampling from instance level and over-sampling from bag level to improve the performance of the multi-instance learning in the diagnosis of DR.
Through empirical evaluation and comparison with different baselinemethods and the state-of-the-art methods on data from Messidor, we illustrate that the proposed method reports favorable results, with an overall classification accuracy of 0.916 and an AUC of 0.957.
The experiments results demonstrate that the proposed multi-kernel multi-instance learning framework with bi-level re-sampling can solve the problem in the imbalanced and weakly supervised data for grading diabetic retinopathy, and it improves the diagnosis performance over several state-of-the-art competing methods.
糖尿病视网膜病变（DR）是糖尿病最严重的并发症之一。早期发现和治疗 DR 是关键的公共卫生干预措施，可以显著降低视力丧失的风险。如何有效地筛选和诊断眼底视网膜图像，以便及时识别视网膜病变，是一个重大挑战。在传统的 DR 筛查系统中，微动脉瘤（MA）和出血（H）病变检测的准确性决定了最终的筛查性能。由于需要保证高灵敏度，检测方法产生了大量的假阳性样本，而分类模型在去除假阳性方面效果不佳，因为可疑病变缺乏标签信息。
为了解决 DR 诊断中的监督学习问题，我们将弱监督多类 DR 分级制定为多类多实例问题，其中每个图像（袋）被标记为健康或异常，并由未标记的候选病变区域（实例）组成。具体来说，我们提出了一种基于图核的多核多实例学习方法。此外，我们从实例级别进行欠采样，并从袋级别进行过采样，以提高多实例学习在 DR 诊断中的性能。
通过在 Messidor 数据上的实证评估和与不同基线方法以及最新方法的比较，我们表明所提出的方法报告了有利的结果，整体分类准确率为 0.916，AUC 为 0.957。
实验结果表明，所提出的具有两级重采样的多核多实例学习框架可以解决分级糖尿病视网膜病变中不平衡和弱监督数据的问题，并且在几个最新的竞争方法上提高了诊断性能。
