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arXiv 2102.07978cs.CVcs.AI
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眼科疾病智能识别的标杆:一枪多病检测

A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection

Nankai University(南开大学);Beijing Shanggong Medical Technology Co. Ltd(北京上工医疗科技有限公司)

Ning Li, Tao Li, Chunyu Hu, Kai Wang, Hong Kang

摘要:

在眼科,早期眼底筛查是预防眼科疾病致盲的一种经济有效的方法。临床上,由于医疗资源匮乏,人工诊断费时费力,可能延误病情。随着深度学习的发展,一些眼科疾病的研究取得了很好的效果,然而,大多数研究都只是基于一种疾病。在眼底筛查中,眼科医生通常会对双眼眼底图像进行多疾病的诊断,为了满足真实的医疗场景,我们发布了一个包含8种疾病的数据集,其中包含5,000名患者双眼的10,000张眼底图像。我们通过一些最先进的深度神经网络对它做了一些基准实验。我们发现单纯增加网络规模并不能很好地解决多疾病分类问题,需要一种结构良好的结合多疾病特征的特征融合方法。希望通过本文的工作,推动相关领域的研究。

英文摘要:

In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases. Clinically, due to the lack of medical resources, manual diagnosis is time-consuming and may delay the condition. With the development of deep learning, some researches on ophthalmic diseases have achieved good results, however, most of them are just based on one disease. During fundus screening, ophthalmologists usually give diagnoses of multi-disease on binocular fundus image, so we release a dataset with 8 diseases to meet the real medical scene, which contains 10,000 fundus images from both eyes of 5,000 patients. We did some benchmark experiments on it through some state-of-the-art deep neural networks. We found simply increasing the scale of network cannot bring good results for multi-disease classification, and a well-structured feature fusion method combines characteristics of multi-disease is needed. Through this work, we hope to advance the research of related fields.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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