The detection of retinal microaneurysms is crucial for the early detection of important diseases such as diabetic retinopathy. However, the detection of these lesions in retinography, the most widely available retinal imaging modality, remains a very challenging task. This is mainly due to the tiny size and low contrast of the microaneurysms in the images. Consequently, the automated detection of microaneurysms usually relies on extensive ad-hoc processing. In this regard, although microaneurysms can be more easily detected using fluorescein angiography, this alternative imaging modality is invasive and not adequate for regular preventive screening.

In this work, we propose a novel deep learning methodology that takes advantage of unlabeled multimodal image pairs for improving the detection of microaneurysms in retinography. In particular, we propose a novel adversarial multimodal pre-training consisting in the prediction of fluorescein angiography from retinography using generative adversarial networks. This pre-training allows learning about the retina and the microaneurysms without any manually annotated data. Additionally, we also propose to approach the microaneurysms detection as a heatmap regression, which allows an efficient detection and precise localization of multiple microaneurysms. To validate and analyze the proposed methodology, we perform an exhaustive experimentation on different public datasets. Additionally, we provide relevant comparisons against different state-of-the-art approaches. The results show a satisfactory performance of the proposal, achieving an Average Precision of 64.90%, 31.36%, and 33.55% in the E-Ophtha, ROC, and DDR public datasets. Overall, the proposed approach outperforms existing deep learning alternatives while providing a more straightforward detection method that can be effectively applied to raw unprocessed retinal images.

视网膜微动脉瘤的检测对于糖尿病视网膜病变等重要疾病的早期发现至关重要。然而，在视网膜成像中检测这些病变，最广泛使用的视网膜成像方式，仍然是一项非常具有挑战性的任务。这主要是由于图像中微动脉瘤的体积小和对比度低。因此，微动脉瘤的自动检测通常依赖于广泛的临时处理。在这方面，虽然使用荧光素血管造影可以更容易地检测微动脉瘤，但这种替代成像方式是侵入性的，不足以进行定期的预防性筛查。

在这项工作中，我们提出了一种新颖的深度学习方法，该方法利用未标记的多模态图像对来改进视网膜成像中微动脉瘤的检测。特别是，我们提出了一种新的对抗多模态预训练，包括使用生成对抗网络从视网膜成像预测荧光素血管造影。这种预训练允许在没有任何手动注释数据的情况下了解视网膜和微动脉瘤。此外，我们还建议将微动脉瘤检测作为热图回归进行处理，这样可以有效检测和精确定位多个微动脉瘤。为了验证和分析所提出的方法，我们对不同的公共数据集进行了详尽的实验。此外，我们提供了与不同最先进方法的相关比较。结果表明，该提案的性能令人满意，在 E-Ophtha、ROC 和 DDR 公共数据集中实现了 64.90%、31.36% 和 33.55% 的平均精度。总体而言，所提出的方法优于现有的深度学习替代方案，同时提供了一种更直接的检测方法，可以有效地应用于未经处理的原始视网膜图像。