A Disease-Specific Foundation Model Using Over 100K Fundus Images: Release and Validation for Abnormality and Multi-Disease Classification on Downstream Tasks

• URL: http://arxiv.org/abs/2408.08790v1
• Date: Fri, 16 Aug 2024 15:03:06 GMT
• Title: A Disease-Specific Foundation Model Using Over 100K Fundus Images: Release and Validation for Abnormality and Multi-Disease Classification on Downstream Tasks
• Authors: Boa Jang, Youngbin Ahn, Eun Kyung Choe, Chang Ki Yoon, Hyuk Jin Choi, Young-Gon Kim,
• Abstract summary: We developed a Fundus-Specific Pretrained Model (Image+Fundus), a supervised artificial intelligence model trained to detect abnormalities in fundus images. A total of 57,803 images were used to develop this pretrained model, which achieved superior performance across various downstream tasks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence applied to retinal images offers significant potential for recognizing signs and symptoms of retinal conditions and expediting the diagnosis of eye diseases and systemic disorders. However, developing generalized artificial intelligence models for medical data often requires a large number of labeled images representing various disease signs, and most models are typically task-specific, focusing on major retinal diseases. In this study, we developed a Fundus-Specific Pretrained Model (Image+Fundus), a supervised artificial intelligence model trained to detect abnormalities in fundus images. A total of 57,803 images were used to develop this pretrained model, which achieved superior performance across various downstream tasks, indicating that our proposed model outperforms other general methods. Our Image+Fundus model offers a generalized approach to improve model performance while reducing the number of labeled datasets required. Additionally, it provides more disease-specific insights into fundus images, with visualizations generated by our model. These disease-specific foundation models are invaluable in enhancing the performance and efficiency of deep learning models in the field of fundus imaging.

Related papers

• Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting [1.2573191100165562]
  We adapted the DINOv2 vision transformer for retinal imaging classification tasks using self-supervised learning. We generated two novel foundation models termed DINORET and BE DINORET. Our few-shot learning studies indicated that DINORET and BE DINORET outperform RETFound in terms of data-efficiency.
  arXiv  Detail & Related papers  (2024-09-25T20:17:16Z)
• EyeFound: A Multimodal Generalist Foundation Model for Ophthalmic Imaging [13.88319807760491]
  We present EyeFound, a multimodal foundation model for ophthalmic images. It learns generalizable representations from unlabeled multimodal retinal images. It is trained on 2.78 million images from 227 hospitals across 11 ophthalmic modalities.
  arXiv  Detail & Related papers  (2024-05-18T17:03:39Z)
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• Enhance Eye Disease Detection using Learnable Probabilistic Discrete Latents in Machine Learning Architectures [1.6000489723889526]
  Ocular diseases, including diabetic retinopathy and glaucoma, present a significant public health challenge. Deep learning models have emerged as powerful tools for analysing medical images, such as retina imaging. Challenges persist in model relibability and uncertainty estimation, which are critical for clinical decision-making.
  arXiv  Detail & Related papers  (2024-01-21T04:14:54Z)
• On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation [47.95611203419802]
  Foundations for vision and language, pre-trained on extensive sets of natural image and text data, have emerged as a promising approach. We compare the generalization performance to unseen domains of various pre-trained models after being fine-tuned on the same in-distribution dataset. We further developed a new Bayesian uncertainty estimation for frozen models and used them as an indicator to characterize the model's performance on out-of-distribution data.
  arXiv  Detail & Related papers  (2023-11-18T14:52:10Z)
• Diffusion Models for Image Restoration and Enhancement -- A Comprehensive Survey [96.99328714941657]
  We present a comprehensive review of recent diffusion model-based methods on image restoration. We classify and emphasize the innovative designs using diffusion models for both IR and blind/real-world IR. We propose five potential and challenging directions for the future research of diffusion model-based IR.
  arXiv  Detail & Related papers  (2023-08-18T08:40:38Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification [41.16626194300303]
  Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications. Recent advances further enable adapting foundation models in downstream tasks efficiently using only a few training samples. Yet, the application of such learning paradigms in medical image analysis remains scarce due to the shortage of publicly accessible data and benchmarks.
  arXiv  Detail & Related papers  (2023-06-16T01:46:07Z)
• Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains [3.8137985834223502]
  Building generative models for medical images that faithfully depict clinical context may help alleviate the paucity of healthcare datasets. We explore the sub-components of the Stable Diffusion pipeline to fine-tune the model to generate medical images. Our best-performing model improves upon the stable diffusion baseline and can be conditioned to insert a realistic-looking abnormality on a synthetic radiology image.
  arXiv  Detail & Related papers  (2022-10-09T01:43:08Z)
• 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)
• 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.