Diagnosis of Multiple Fundus Disorders Amidst a Scarcity of Medical Experts Via Self-supervised Machine Learning

• URL: http://arxiv.org/abs/2404.13388v2
• Date: Tue, 23 Apr 2024 13:25:01 GMT
• Title: Diagnosis of Multiple Fundus Disorders Amidst a Scarcity of Medical Experts Via Self-supervised Machine Learning
• Authors: Yong Liu, Mengtian Kang, Shuo Gao, Chi Zhang, Ying Liu, Shiming Li, Yue Qi, Arokia Nathan, Wenjun Xu, Chenyu Tang, Edoardo Occhipinti, Mayinuer Yusufu, Ningli Wang, Weiling Bai, Luigi Occhipinti,
• Abstract summary: Fundus diseases are major causes of visual impairment and blindness worldwide. We propose a general self-supervised machine learning framework that can handle diverse fundus diseases from unlabeled fundus images.
• Score: 13.174267261284733
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fundus diseases are major causes of visual impairment and blindness worldwide, especially in underdeveloped regions, where the shortage of ophthalmologists hinders timely diagnosis. AI-assisted fundus image analysis has several advantages, such as high accuracy, reduced workload, and improved accessibility, but it requires a large amount of expert-annotated data to build reliable models. To address this dilemma, we propose a general self-supervised machine learning framework that can handle diverse fundus diseases from unlabeled fundus images. Our method's AUC surpasses existing supervised approaches by 15.7%, and even exceeds performance of a single human expert. Furthermore, our model adapts well to various datasets from different regions, races, and heterogeneous image sources or qualities from multiple cameras or devices. Our method offers a label-free general framework to diagnose fundus diseases, which could potentially benefit telehealth programs for early screening of people at risk of vision loss.

Related papers

• Privacy-Preserving Federated Foundation Model for Generalist Ultrasound Artificial Intelligence [83.02106623401885]
  We present UltraFedFM, an innovative privacy-preserving ultrasound foundation model. UltraFedFM is collaboratively pre-trained using federated learning across 16 distributed medical institutions in 9 countries. It achieves an average area under the receiver operating characteristic curve of 0.927 for disease diagnosis and a dice similarity coefficient of 0.878 for lesion segmentation.
  arXiv  Detail & Related papers  (2024-11-25T13:40:11Z)
• EyeDiff: text-to-image diffusion model improves rare eye disease diagnosis [7.884451100342276]
  EyeDiff is a text-to-image model designed to generate multimodal ophthalmic images from natural language prompts. EyeDiff is trained on eight large-scale datasets and is adapted to ten multi-country external datasets.
  arXiv  Detail & Related papers  (2024-11-15T07:30:53Z)
• GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis [52.04899592688968]
  We propose GraVIS, which is specifically optimized for learning self-supervised features from dermatology images. GraVIS significantly outperforms its transfer learning and self-supervised learning counterparts in both lesion segmentation and disease classification tasks.
  arXiv  Detail & Related papers  (2023-01-11T11:38:37Z)
• RADNet: Ensemble Model for Robust Glaucoma Classification in Color Fundus Images [0.0]
  Glaucoma is one of the most severe eye diseases, characterized by rapid progression and leading to irreversible blindness. Regular glaucoma screenings of the population shall improve early-stage detection, however the desirable frequency of etymological checkups is often not feasible. In our work, we propose an advanced image pre-processing technique combined with an ensemble of deep classification networks.
  arXiv  Detail & Related papers  (2022-05-25T16:48:00Z)
• Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging [63.62764375279861]
  This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data. We propose the use of autoencoder architectures to investigate two strategies for disease detection.
  arXiv  Detail & Related papers  (2021-12-20T13:29:13Z)
• 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)
• 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)
• A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection [9.059366200759722]
  In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases. We release a dataset with 8 diseases to meet the real medical scene, which contains 10,000 fundus images from both eyes of 5,000 patients.
  arXiv  Detail & Related papers  (2021-02-16T07:00:49Z)
• Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis [28.428216831922228]
  This paper presents a novel self-supervised feature learning method by effectively exploiting multi-modal data for retinal disease diagnosis. Our objective learns both modality-invariant features and patient-similarity features. We evaluate our method on two public benchmark datasets for retinal disease diagnosis.
  arXiv  Detail & Related papers  (2020-07-21T19:49:45Z)
• 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.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.