RetinaRegen: A Hybrid Model for Readability and Detail Restoration in Fundus Images

• URL: http://arxiv.org/abs/2502.19153v2
• Date: Thu, 27 Feb 2025 06:41:58 GMT
• Title: RetinaRegen: A Hybrid Model for Readability and Detail Restoration in Fundus Images
• Authors: Yuhan Tang, Yudian Wang, Weizhen Li, Ye Yue, Chengchang Pan, Honggang Qi,
• Abstract summary: RetinaRegen is a hybrid model for retinal image restoration that integrates a readability classifi-cation model, a Diffusion Model, and a Variational Autoencoder.<n>Ex-periments on the SynFundus-1M dataset show that the proposed method achieves a PSNR of 27.4521, an SSIM of 0.9556, and an LPIPS of 0.1911 for the readability labels of the optic disc.
• Score: 4.7244823473263615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fundus image quality is crucial for diagnosing eye diseases, but real-world conditions often result in blurred or unreadable images, increasing diagnostic uncertainty. To address these challenges, this study proposes RetinaRegen, a hybrid model for retinal image restoration that integrates a readability classifi-cation model, a Diffusion Model, and a Variational Autoencoder (VAE). Ex-periments on the SynFundus-1M dataset show that the proposed method achieves a PSNR of 27.4521, an SSIM of 0.9556, and an LPIPS of 0.1911 for the readability labels of the optic disc (RO) region. These results demonstrate superior performance in restoring key regions, offering an effective solution to enhance fundus image quality and support clinical diagnosis.

Related papers

• Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis [55.959002385347645]
  Latent Drifting enables diffusion models to be conditioned for medical images fitted for the complex task of counterfactual image generation. We evaluate our method on three public longitudinal benchmark datasets of brain MRI and chest X-rays for counterfactual image generation.
  arXiv  Detail & Related papers  (2024-12-30T01:59:34Z)
• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  We propose a unified MRI reconstruction model robust to various measurement undersampling patterns and image resolutions. Our model improves SSIM by 11% and PSNR by 4 dB over a state-of-the-art CNN (End-to-End VarNet) with 600$times$ faster inference than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Graph-Guided Test-Time Adaptation for Glaucoma Diagnosis using Fundus Photography [36.328434151676525]
  Glaucoma is a leading cause of irreversible blindness worldwide. Deep learning approaches using fundus images have largely improved early diagnosis of glaucoma. Variations in images from different devices and locations (known as domain shifts) challenge the use of pre-trained models in real-world settings. We propose a novel Graph-guided Test-Time Adaptation framework to generalize glaucoma diagnosis models to unseen test environments.
  arXiv  Detail & Related papers  (2024-07-05T10:06:55Z)
• 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)
• Step-Calibrated Diffusion for Biomedical Optical Image Restoration [47.191704042917394]
  Restorative Step-Calibrated Diffusion (RSCD) is an unpaired diffusion-based image restoration method.<n>RSCD outperforms other widely used unpaired image restoration methods on both image quality and perceptual evaluation.<n>RSCD improves performance on downstream clinical imaging tasks, including automated brain tumor diagnosis and deep tissue imaging.
  arXiv  Detail & Related papers  (2024-03-20T15:38:53Z)
• An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images [4.640835690336653]
  We propose an ensemble method to automatically grade Diabetic retinopathy (DR) images available from Diabetic Retinopathy Analysis Challenge (DRAC) 2022. First, we adopt the state-of-the-art classification networks, and train them to grade UW- OCTA images with different splits of the available dataset. Ultimately, we obtain 25 models, of which, the top 16 models are selected and ensembled to generate the final predictions.
  arXiv  Detail & Related papers  (2022-12-12T22:06:47Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• RFormer: Transformer-based Generative Adversarial Network for Real Fundus Image Restoration on A New Clinical Benchmark [8.109057397954537]
  Ophthalmologists have used fundus images to screen and diagnose eye diseases. Low-quality (LQ) degraded fundus images easily lead to uncertainty in clinical screening and generally increase the risk of misdiagnosis. We propose a novel Transformer-based Generative Adversarial Network (RFormer) to restore the real degradation of clinical fundus images.
  arXiv  Detail & Related papers  (2022-01-03T03:56:58Z)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution [65.25508348574974]
  We go below the MRI acceleration factors reported by all published papers that reference the original fastMRI challenge. We consider powerful deep learning based image enhancement methods to compensate for the underresolved images. The quality of the reconstructed images surpasses that of the other methods, yielding an MSE of 0.00114, a PSNR of 29.6 dB, and an SSIM of 0.956 at x16 acceleration factor.
  arXiv  Detail & Related papers  (2021-03-04T10:45:01Z)
• 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.