Ophthalmic diseases represent a significant global health issue, necessitating the use of advanced precise diagnostic tools. Optical Coherence Tomography (OCT) imagery which offers high-resolution cross-sectional images of the retina has become a pivotal imaging modality in ophthalmology. Traditionally physicians have manually detected various diseases and biomarkers from such diagnostic imagery. In recent times, deep learning techniques have been extensively used for medical diagnostic tasks enabling fast and precise diagnosis. This paper presents a novel approach for ophthalmic biomarker detection using an ensemble of Convolutional Neural Network (CNN) and Vision Transformer. While CNNs are good for feature extraction within the local context of the image, transformers are known for their ability to extract features from the global context of the image. Using an ensemble of both techniques allows us to harness the best of both worlds. Our method has been implemented on the OLIVES dataset to detect 6 major biomarkers from the OCT images and shows significant improvement of the macro averaged F1 score on the dataset.

翻译：眼科疾病是全球性的重大健康问题，亟需采用先进、精准的诊断工具。光学相干断层扫描（OCT）影像能够提供视网膜的高分辨率横截面图像，已成为眼科领域的关键成像模态。传统上，医生需从此类诊断影像中人工检测多种疾病及生物标志物。近年来，深度学习技术已广泛应用于医疗诊断任务，实现了快速而精准的诊断。本文提出一种新颖的眼科生物标志物检测方法，该方法集成使用了卷积神经网络（CNN）与视觉Transformer。CNN擅长提取图像局部上下文中的特征，而Transformer则以提取图像全局上下文特征的能力著称。通过集成这两种技术，我们的方法能够充分发挥二者的优势。我们在OLIVES数据集上实现了该方法，用于从OCT图像中检测6种主要生物标志物，并在该数据集上实现了宏观平均F1分数的显著提升。