Balanced Soft mixture-of-expert model for Glaucoma Detection

• URL: http://arxiv.org/abs/2607.25324v1
• Date: Tue, 28 Jul 2026 06:19:59 GMT
• Title: Balanced Soft mixture-of-expert model for Glaucoma Detection
• Abstract summary: Glaucoma is a leading cause of irreversible vision loss.<n>Deep learning based uni-modal models have improved the accuracy and efficiency of glaucoma detection.
• Score: 5.433118246208783
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Glaucoma is a group of eye diseases that damage the optic nerve, often caused by elevated intraocular pressure. It is a leading cause of irreversible vision loss and is typically developed slowly and painlessly, making it difficult to notice until significant damage has occurred. Therefore, early detection is crucial to prevent or slow the progression of vision loss. In recent years, deep learning based uni-modal models have improved the accuracy and efficiency of glaucoma detection, empowering doctors with tools for earlier diagnosis, better monitoring, and timely treatment. Building on this, multi-modal models have emerged, leveraging the strengths of different imaging modalities to learn richer and more robust representations, further enhancing glaucoma detection accuracy. However, multi-modal learning faces challenges such as imbalanced and under-optimized uni-modal representations due to joint learning objectives. To address this, we propose a balanced soft mixture-experts model with three experts and load balancing loss. The performance is measured by AUC, our proposed method surpasses the performance of all uni-modal baselines, conventional multi-modal models, and current stateof- the-art balanced multi-modal models. The proposed model can be generalized to other disease detections such as diabetic retinopathy.

Related papers

• DiffSight-Former: Modeling Structural Differences and Temporal Dynamics for Glaucoma Progression Prediction [15.917908024992805]
  Glaucoma is a leading cause of irreversible blindness worldwide.<n>Deep learning has achieved promising performance in fundus image analysis.<n>We propose DiffSight-Former, a framework for glaucoma progression prediction from sequential fundus images.
  arXiv  Detail & Related papers  (2026-06-08T07:39:10Z)
• Temporal-Enhanced Interpretable Multi-Modal Prognosis and Risk Stratification Framework for Diabetic Retinopathy (TIMM-ProRS) [0.17398560678845076]
  This study introduces TIMM-ProRS, a novel deep learning framework integrating Vision Transformer (ViT), Convolutional Neural Network (CNN), and Graph Neural Network (GNN)<n>TIMM-ProRS uniquely leverages both retinal images and temporal biomarkers (HbA1c, retinal thickness) to capture multi-modal and temporal dynamics.<n>The model achieves 97.8% accuracy and an F1-score of 0.96, demonstrating state-of-the-art performance and outperforming existing methods like RSG-Net and DeepDR.
  arXiv  Detail & Related papers  (2026-01-13T05:57:47Z)
• EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis [20.318178211934985]
  We propose EyeCLIP, a visual-language foundation model developed using over 2.77 million ophthalmology images with partial text data. EyeCLIP can be transferred to a wide range of downstream tasks involving ocular and systemic diseases.
  arXiv  Detail & Related papers  (2024-09-10T17:00:19Z)
• Confidence-aware multi-modality learning for eye disease screening [58.861421804458395]
  We propose a novel multi-modality evidential fusion pipeline for eye disease screening. It provides a measure of confidence for each modality and elegantly integrates the multi-modality information. Experimental results on both public and internal datasets demonstrate that our model excels in robustness.
  arXiv  Detail & Related papers  (2024-05-28T13:27:30Z)
• 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)
• 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)
• Multi-scale Spatio-temporal Transformer-based Imbalanced Longitudinal Learning for Glaucoma Forecasting from Irregular Time Series Images [45.894671834869975]
  Glaucoma is one of the major eye diseases that leads to progressive optic nerve fiber damage and irreversible blindness. We introduce the Multi-scale Spatio-temporal Transformer Network (MST-former) based on the transformer architecture tailored for sequential image inputs. Our method shows excellent generalization capability on the Alzheimer's Disease Neuroimaging Initiative (ADNI) MRI dataset, with an accuracy of 90.3% for mild cognitive impairment and Alzheimer's disease prediction.
  arXiv  Detail & Related papers  (2024-02-21T02:16:59Z)
• 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.<n>Deep learning models have emerged as powerful tools for analysing medical images, such as retina imaging.<n>Challenges persist in model relibability and uncertainty estimation, which are critical for clinical decision-making.
  arXiv  Detail & Related papers  (2024-01-21T04:14:54Z)
• Reliable Multimodality Eye Disease Screening via Mixture of Student's t Distributions [49.4545260500952]
  We introduce a novel multimodality evidential fusion pipeline for eye disease screening, EyeMoSt. Our model estimates both local uncertainty for unimodality and global uncertainty for the fusion modality to produce reliable classification results. Our experimental findings on both public and in-house datasets show that our model is more reliable than current methods.
  arXiv  Detail & Related papers  (2023-03-17T06:18:16Z)
• On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
  We compare the impact of different training procedures for diabetic retinopathy grading. We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions. Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
  arXiv  Detail & Related papers  (2021-06-25T08:32:45Z)
• 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.