A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection

• URL: http://arxiv.org/abs/2102.07978v1
• Date: Tue, 16 Feb 2021 07:00:49 GMT
• Title: A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection
• Authors: Ning Li, Tao Li, Chunyu Hu, Kai Wang, Hong Kang
• Abstract summary: 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.
• Score: 9.059366200759722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases. Clinically, due to the lack of medical resources, manual diagnosis is time-consuming and may delay the condition. With the development of deep learning, some researches on ophthalmic diseases have achieved good results, however, most of them are just based on one disease. During fundus screening, ophthalmologists usually give diagnoses of multi-disease on binocular fundus image, so 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. We did some benchmark experiments on it through some state-of-the-art deep neural networks. We found simply increasing the scale of network cannot bring good results for multi-disease classification, and a well-structured feature fusion method combines characteristics of multi-disease is needed. Through this work, we hope to advance the research of related fields.

Related papers

• LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models [38.78576472811659]
  Large vision-language models (LVLMs) have the potential to assist in understanding anatomical information, diagnosing eye diseases, and drafting interpretations and follow-up plans. We benchmarked 13 state-of-the-art LVLM representatives from closed-source, open-source, and medical domains. The results demonstrate a significant performance drop for LVLMs in ophthalmology compared to other domains.
  arXiv  Detail & Related papers  (2024-10-02T14:57:58Z)
• Common and Rare Fundus Diseases Identification Using Vision-Language Foundation Model with Knowledge of Over 400 Diseases [57.27458882764811]
  Previous foundation models for retinal images were pre-trained with limited disease categories and knowledge base. To RetiZero's pre-training, we compiled 341,896 fundus images paired with text descriptions, sourced from public datasets, ophthalmic literature, and online resources. RetiZero exhibits superior performance in several downstream tasks, including zero-shot disease recognition, image-to-image retrieval, and internal- and cross-domain disease identification.
  arXiv  Detail & Related papers  (2024-06-13T16:53:57Z)
• A better approach to diagnose retinal diseases: Combining our Segmentation-based Vascular Enhancement with deep learning features [3.717366858126521]
  Abnormalities in retinal fundus images may indicate certain pathologies. In conventional medicine, the diagnosis of retina-related diseases relies on a physician's subjective assessment of the retinal fundus images. This paper proposes a fast, objective, and accurate method for the diagnosis of diseases related to retinal fundus images.
  arXiv  Detail & Related papers  (2024-05-25T13:52:40Z)
• Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling [49.52787013516891]
  Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging. A temporal attention analysis also suggested that, while the most recent image is typically the most influential, prior imaging still provides additional prognostic value.
  arXiv  Detail & Related papers  (2024-05-14T17:15:28Z)
• Diagnosis of Multiple Fundus Disorders Amidst a Scarcity of Medical Experts Via Self-supervised Machine Learning [13.174267261284733]
  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.
  arXiv  Detail & Related papers  (2024-04-20T14:15:25Z)
• A CNN-LSTM Combination Network for Cataract Detection using Eye Fundus Images [0.0]
  One of the leading causes of irreversible blindness in persons over the age of 50 is delayed cataract treatment. We developed a CNN-LSTM-based model architecture with the goal of creating a low-cost diagnostic system. The suggested architecture outperformed previous systems with a state-of-the-art 97.53% accuracy.
  arXiv  Detail & Related papers  (2022-10-28T12:35:15Z)
• GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges [48.98620387924817]
  We set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. We have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for glaucoma grading.
  arXiv  Detail & Related papers  (2022-02-14T06:54:15Z)
• 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)
• Efficient Screening of Diseased Eyes based on Fundus Autofluorescence Images using Support Vector Machine [0.12189422792863448]
  A variety of vision ailments are associated with geographic atrophy (GA) in the foveal region of the eye. In current clinical practice, the ophthalmologist manually detects potential presence of such GA based on fundus autofluorescence (FAF) images. We propose a screening step, where healthy and diseased eyes are algorithmically differentiated with limited input from only optometrists.
  arXiv  Detail & Related papers  (2021-04-17T11:54:34Z)
• 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.

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