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

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

URL: http://arxiv.org/abs/2305.07816v1
Date: Sat, 13 May 2023 02:00:06 GMT
Title: PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation
Authors: Huihui Fang, Fei Li, Junde Wu, Huazhu Fu, Xu Sun, Jos\'e Ignacio Orlando, Hrvoje Bogunovi\'c, Xiulan Zhang, Yanwu Xu
Abstract summary: Pathologic myopia (PM) is a common myopic retinal degeneration suffered by highly blinding population. This paper provides insights about PALM, our open fundus imaging dataset for pathological recognition and anatomical structure annotation.
Score: 41.80715213373843
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
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.

Related papers

OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods [34.13887472397715]
This work presents an open-access OCT dataset ( OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology. The dataset consists of OCT records of patients with Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO), Retinal Vein OcclusionRVO, and Vitreomacular Interface Disease (VID)
arXiv Detail & Related papers (2023-12-13T16:18:40Z)
AMLP:Adaptive Masking Lesion Patches for Self-supervised Medical Image Segmentation [67.97926983664676]
Self-supervised masked image modeling has shown promising results on natural images. However, directly applying such methods to medical images remains challenging. We propose a novel self-supervised medical image segmentation framework, Adaptive Masking Lesion Patches (AMLP)
arXiv Detail & Related papers (2023-09-08T13:18:10Z)
Statistical and Topological Summaries Aid Disease Detection for Segmented Retinal Vascular Images [0.0]
Microvascular diseases are assessed by visual inspection of retinal images. This can be challenging when diseases exhibit silent symptoms or patients cannot attend in-person meetings. We examine the performance of machine learning algorithms in detecting microvascular disease.
arXiv Detail & Related papers (2022-02-20T01:29:36Z)
A deep learning model for classification of diabetic retinopathy in eye fundus images based on retinal lesion detection [0.0]
Diabetic retinopathy (DR) is the result of a complication of diabetes affecting the retina. It can cause blindness, if left undiagnosed and untreated. This paper presents a model for automatic DR classification on eye fundus images.
arXiv Detail & Related papers (2021-10-14T22:04:59Z)
Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders [63.391402501241195]
glaucoma is challenging to detect since it remains asymptomatic until the symptoms are severe. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and analyzing high-level information directly to classify the data.
arXiv Detail & Related papers (2021-10-04T16:06:49Z)
Non-contact Pain Recognition from Video Sequences with Remote Physiological Measurements Prediction [53.03469655641418]
We present a novel multi-task learning framework which encodes both appearance changes and physiological cues in a non-contact manner for pain recognition. We establish the state-of-the-art performance of non-contact pain recognition on publicly available pain databases.
arXiv Detail & Related papers (2021-05-18T20:47:45Z)
An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
arXiv Detail & Related papers (2021-03-02T13:14:15Z)
Modeling and Enhancing Low-quality Retinal Fundus Images [167.02325845822276]
Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. We propose a clinically oriented fundus enhancement network (cofe-Net) to suppress global degradation factors. Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details.
arXiv Detail & Related papers (2020-05-12T08:01:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.