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ctmedtech/PALM

🩺 PALM — Pathologic Myopia Fundus Image Dataset Image: Dataset Samples. 📘 Overview PALM (Pathologic Myopia) is a publicly available fundus image dataset developed for detecting pathologic myopia (PM) and analyzing associated retinal lesions and anatomical structures. It was released for the Pathologic Myopia Challenge (PALM), hosted by the Chinese Academy of Sciences and Sun Yat-sen University… See the full description on the dataset page: https://huggingface.co/datasets/ctmedtech/PALM.

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Dataset Card

🩺 PALM — Pathologic Myopia Fundus Image Dataset

<table align="center"> <tr> <td width="100%" align="center"> <img src="rmimages/MergedFundusImageswith_Captions.jpg" alt="Merged Dataset Samples" style="max-width: 100%; height: auto;"> <br> <p><strong>Image:</strong> Dataset Samples.</p> </td> </tr> </table>

📘 Overview

PALM (Pathologic Myopia) is a publicly available fundus image dataset developed for detecting pathologic myopia (PM) and analyzing associated retinal lesions and anatomical structures.

It was released for the Pathologic Myopia Challenge (PALM), hosted by the Chinese Academy of Sciences and Sun Yat-sen University, and published on IEEE DataPort. The dataset provides both classification and segmentation tasks, making it valuable for multi-task ophthalmic AI research.

🔗 Official Sources:

📊 Dataset Summary

Feature	Description
Images	1,200 color fundus photographs
Labels	Binary — Pathologic Myopia (PM) or Non-PM
Annotations	Optic disc boundary, fovea location, and lesion masks (atrophy, detachment)
Resolution	Varies (45° field-of-view fundus images)
Format	JPEG
Tasks	Classification, Segmentation
Source Institutions	Multiple ophthalmic centers in China
License	Free for research and educational use
Released	2019 (PALM Challenge)

📁 Folder Structure

python

PALM/
├── images/
│ ├── train/
│ ├── validation/
│ └── test/
├── annotations/
│ ├── optic_disc_masks/
│ ├── lesion_masks/
│ └── fovea_locations.csv
├── labels.csv
└── README.md

🩸 Labels and Annotations

—Classification Label:
—1 = Pathologic Myopia (PM)
—0 = Non-Pathologic (Normal)

—Segmentation Annotations:
—Optic disc boundaries
—Fovea coordinates
—Lesion masks for:
—Patchy atrophy
—Retinal detachment
—Peripapillary atrophy

🧠 Research Applications

PALM is designed for:

—Pathologic Myopia detection from fundus images
—Segmentation of optic disc, fovea, and lesion regions
—Multi-task learning combining classification and segmentation
—Explainable AI studies on high-myopia pathology

⚙️ Limitations

—Limited image count (~1,200)
—Variability in camera type and illumination
—Binary labeling (PM vs Non-PM) does not cover all clinical myopia subtypes
—Lesion annotations may need preprocessing for some segmentation frameworks

📥 Access and Citation

🔗 Access

Dataset available via official IEEE DataPort page: 👉 https://ieee-dataport.org/documents/palm-pathologic-myopia-challenge

📄 Citation

If you use PALM, please cite: Fang H., Li F., Wu J., Fu H., Sun X., Orlando J. I., Bogunović H., Zhang X., Xu Y. PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation. IEEE DataPort, 2019.

🧾 License

The PALM dataset is made available for research and educational use only. Redistribution or commercial use requires permission from the dataset authors.