Improving AMD diagnosis by the simultaneous identification of associated retinal lesions

TL;DR

This paper introduces a CNN-based method for simultaneous AMD diagnosis and retinal lesion classification, enhancing detection accuracy and providing interpretable lesion information to aid early screening and diagnosis.

Contribution

It presents a novel CNN approach that jointly detects AMD and classifies associated retinal lesions, a task not previously addressed in this domain.

Findings

Improves AMD detection accuracy.

Achieves satisfactory lesion classification results.

Utilizes easily obtainable image-level labels.

Abstract

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.