Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection

• URL: http://arxiv.org/abs/2309.00824v1
• Date: Sat, 2 Sep 2023 04:42:08 GMT
• Title: Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection
• Authors: D. Dhinakaran, L. Srinivasan, D. Selvaraj, S. M. Udhaya Sankar
• Abstract summary: Diabetic Retinopathy (DR) is a significant cause of blindness globally, highlighting the urgent need for early detection and effective treatment. Recent advancements in Machine Learning (ML) techniques have shown promise in DR detection, but the availability of labeled data often limits their performance. This research proposes a novel Semi-Supervised Graph Learning SSGL algorithm tailored for DR detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diabetic Retinopathy (DR) is a significant cause of blindness globally, highlighting the urgent need for early detection and effective treatment. Recent advancements in Machine Learning (ML) techniques have shown promise in DR detection, but the availability of labeled data often limits their performance. This research proposes a novel Semi-Supervised Graph Learning SSGL algorithm tailored for DR detection, which capitalizes on the relationships between labelled and unlabeled data to enhance accuracy. The work begins by investigating data augmentation and preprocessing techniques to address the challenges of image quality and feature variations. Techniques such as image cropping, resizing, contrast adjustment, normalization, and data augmentation are explored to optimize feature extraction and improve the overall quality of retinal images. Moreover, apart from detection and diagnosis, this work delves into applying ML algorithms for predicting the risk of developing DR or the likelihood of disease progression. Personalized risk scores for individual patients are generated using comprehensive patient data encompassing demographic information, medical history, and retinal images. The proposed Semi-Supervised Graph learning algorithm is rigorously evaluated on two publicly available datasets and is benchmarked against existing methods. Results indicate significant improvements in classification accuracy, specificity, and sensitivity while demonstrating robustness against noise and outlie rs.Notably, the proposed algorithm addresses the challenge of imbalanced datasets, common in medical image analysis, further enhancing its practical applicability.

Related papers

• Deep Learning to Predict Glaucoma Progression using Structural Changes in the Eye [0.20718016474717196]
  Glaucoma is a chronic eye disease characterized by optic neuropathy, leading to irreversible vision loss. Early detection is crucial to monitor atrophy and develop treatment strategies to prevent further vision impairment. In this study, we use deep learning models to identify complex disease traits and progression criteria.
  arXiv  Detail & Related papers  (2024-06-09T01:12:41Z)
• Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection [11.980634373191542]
  Distributed training can facilitate the processing of large medical image datasets, and improve the accuracy and efficiency of disease diagnosis. This paper presents an innovative approach to medical image classification, leveraging Federated Learning (FL) to address the dual challenges of data privacy and efficient disease diagnosis.
  arXiv  Detail & Related papers  (2024-04-15T09:07:19Z)
• Detection and Classification of Diabetic Retinopathy using Deep Learning Algorithms for Segmentation to Facilitate Referral Recommendation for Test and Treatment Prediction [0.0]
  This research paper addresses the critical challenge of diabetic retinopathy (DR), a severe complication of diabetes leading to potential blindness. The proposed methodology leverages transfer learning with convolutional neural networks (CNNs) for automatic DR detection using a single fundus photograph. High evaluation scores in Jaccard, F1, recall, precision, and accuracy underscore the model's potential for enhancing diagnostic capabilities in retinal pathology assessment.
  arXiv  Detail & Related papers  (2024-01-05T11:19:24Z)
• ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models [69.9178140563928]
  Colonoscopy analysis is essential for assisting clinical diagnosis and treatment. The scarcity of annotated data limits the effectiveness and generalization of existing methods. We propose an Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy images that benefit the downstream tasks.
  arXiv  Detail & Related papers  (2023-09-03T07:55:46Z)
• DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images [51.27125547308154]
  We organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022) The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge.
  arXiv  Detail & Related papers  (2023-04-05T12:04:55Z)
• Bag of Tricks for Developing Diabetic Retinopathy Analysis Framework to Overcome Data Scarcity [6.802798389355481]
  We present a study for diabetic retinopathy (DR) analysis tasks, including lesion segmentation, image quality assessment, and DR grading. For each task, we introduce a robust training scheme by leveraging ensemble learning, data augmentation, and semi-supervised learning. We propose reliable pseudo labeling that excludes uncertain pseudo-labels based on the model's confidence scores to reduce the negative effect of noisy pseudo-labels.
  arXiv  Detail & Related papers  (2022-10-18T03:25:00Z)
• About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data Annotations [2.3204178451683264]
  Variance Aware Training (VAT) method exploits this property by introducing the variance error into the model loss function. We validate VAT on three medical imaging datasets from diverse domains and various learning objectives.
  arXiv  Detail & Related papers  (2021-05-28T21:34:04Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• 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)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

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.