PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation

TL;DR

This paper introduces PALM, a comprehensive open dataset of 1200 fundus images with annotations for pathologic myopia and anatomical structures, aiming to facilitate AI-based early screening and diagnosis.

Contribution

The paper presents PALM, a new annotated fundus image dataset for pathologic myopia recognition and anatomical structure analysis, supporting AI research in ophthalmology.

Findings

Dataset includes 1200 images with detailed annotations.

Provides insights into labeling process and image quality.

Facilitates development of AI diagnostic tools.

Abstract

Pathologic myopia (PM) is a common blinding retinal degeneration suffered by highly myopic population. Early screening of this condition can reduce the damage caused by the associated fundus lesions and therefore prevent vision loss. Automated diagnostic tools based on artificial intelligence methods can benefit this process by aiding clinicians to identify disease signs or to screen mass populations using color fundus photographs as inputs. This paper provides insights about PALM, our open fundus imaging dataset for pathological myopia recognition and anatomical structure annotation. Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment. In addition, this paper elaborates on other details such as the labeling process used to construct the database, the quality and characteristics of the samples and provides other relevant usage notes.