Explainable Artificial Intelligence (AI) in the form of an interpretable and semiautomatic approach to stage grading ocular pathologies such as Diabetic retinopathy, Hypertensive retinopathy, and other retinopathies on the backdrop of major systemic diseases. The experimental study aims to evaluate an explainable staged grading process without using deep Convolutional Neural Networks (CNNs) directly. Many current CNN-based deep neural networks used for diagnosing retinal disorders might have appreciable performance but fail to pinpoint the basis driving their decisions. To improve these decisions' transparency, we have proposed a clinician-in-the-loop assisted intelligent workflow that performs a retinal vascular assessment on the fundus images to derive quantifiable and descriptive parameters. The retinal vessel parameters meta-data serve as hyper-parameters for better interpretation and explainability of decisions. The semiautomatic methodology aims to have a federated approach to AI in healthcare applications with more inputs and interpretations from clinicians. The baseline process involved in the machine learning pipeline through image processing techniques for optic disc detection, vessel segmentation, and arteriole/venule identification.
翻译:实验研究的目的是评价可解释的分阶段分级过程,而不直接使用深层进化神经网络(CNNs) 。许多目前以CNN为基础的用于诊断视网膜紊乱的深神经网络可能表现良好,但未能确定决定的驱动基础。为了提高这些决定的透明度,我们提议了一种由临床医生协助的智能工作流程,对基金图象进行视波性评估,以得出可量化和描述的参数。视网膜参数元数据作为超常参数,以更好地解释和解释决定。半自动方法的目的是在医疗应用中采用有更多临床医生投入和解释的节能方法,在保健应用中采用节能方法。通过光碟检测、船舶分解和麻醉剂识别的图像处理技术,对机器管道进行基线学习。