Age-related Macular Degeneration (AMD) is the predominant cause of blindness in developed countries, specially in elderly people. Moreover, its prevalence is increasing due to the global population ageing. In this scenario, early detection is crucial to avert later vision impairment. Nonetheless, implementing large-scale screening programmes is usually not viable, since the population at-risk is large and the analysis must be performed by expert clinicians. Also, the diagnosis of AMD is considered to be particularly difficult, as it is characterized by many different lesions that, in many cases, resemble those of other macular diseases. To overcome these issues, several works have proposed automatic methods for the detection of AMD in retinography images, the most widely used modality for the screening of the disease. Nowadays, most of these works use Convolutional Neural Networks (CNNs) for the binary classification of images into AMD and non-AMD classes. In this work, we propose a novel approach based on CNNs that simultaneously performs AMD diagnosis and the classification of its potential lesions. This latter secondary task has not yet been addressed in this domain, and provides complementary useful information that improves the diagnosis performance and helps understanding the decision. A CNN model is trained using retinography images with image-level labels for both AMD and lesion presence, which are relatively easy to obtain. The experiments conducted in several public datasets show that the proposed approach improves the detection of AMD, while achieving satisfactory results in the identification of most lesions.
翻译:此外,由于全球人口老化,早期发现对于避免后期视力受损至关重要。然而,实施大规模筛查方案通常不可行,因为风险人口众多,分析必须由专科临床医生进行。此外,诊断与年龄有关的肌肉变形(AMD)被认为是特别困难的,因为在许多情况中,这种变形有许多不同的损伤特征与其他肌肉疾病相似。为了克服这些问题,一些工作提出了在重塑图像中检测AMD的自动方法,这是最广泛使用的疾病筛查模式。现在,这些工程大多使用动态神经网络将图像进行二进制分类到AMD和非AMD班。在这项工作中,我们建议采用基于CNN的新型方法,同时进行AMD的诊断和潜在损伤的分类。为克服这些问题,有些工作尚未解决后一项次要任务,在重塑图像中提出自动检测AMD的自动方法,这是最广泛使用的模式。在重塑图像中提供有用的补充信息,在对公众诊断性效果进行相对升级的测试时,A项测试有助于了解如何改进图像的正确性分析。在进行这些图像的重新定位时,同时进行一些新的分析。