ETSCL: An Evidence Theory-Based Supervised Contrastive Learning Framework for Multi-modal Glaucoma Grading

• URL: http://arxiv.org/abs/2407.14230v1
• Date: Fri, 19 Jul 2024 11:57:56 GMT
• Title: ETSCL: An Evidence Theory-Based Supervised Contrastive Learning Framework for Multi-modal Glaucoma Grading
• Authors: Zhiyuan Yang, Bo Zhang, Yufei Shi, Ningze Zhong, Johnathan Loh, Huihui Fang, Yanwu Xu, Si Yong Yeo,
• Abstract summary: Glaucoma is one of the leading causes of vision impairment. It remains challenging to extract reliable features due to the high similarity of medical images and the unbalanced multi-modal data distribution. We propose a novel framework, namely ETSCL, which consists of a contrastive feature extraction stage and a decision-level fusion stage.
• Score: 7.188153974946432
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Glaucoma is one of the leading causes of vision impairment. Digital imaging techniques, such as color fundus photography (CFP) and optical coherence tomography (OCT), provide quantitative and noninvasive methods for glaucoma diagnosis. Recently, in the field of computer-aided glaucoma diagnosis, multi-modality methods that integrate the CFP and OCT modalities have achieved greater diagnostic accuracy compared to single-modality methods. However, it remains challenging to extract reliable features due to the high similarity of medical images and the unbalanced multi-modal data distribution. Moreover, existing methods overlook the uncertainty estimation of different modalities, leading to unreliable predictions. To address these challenges, we propose a novel framework, namely ETSCL, which consists of a contrastive feature extraction stage and a decision-level fusion stage. Specifically, the supervised contrastive loss is employed to enhance the discriminative power in the feature extraction process, resulting in more effective features. In addition, we utilize the Frangi vesselness algorithm as a preprocessing step to incorporate vessel information to assist in the prediction. In the decision-level fusion stage, an evidence theory-based multi-modality classifier is employed to combine multi-source information with uncertainty estimation. Extensive experiments demonstrate that our method achieves state-of-the-art performance. The code is available at \url{https://github.com/master-Shix/ETSCL}.

Related papers

• Simultaneous Tri-Modal Medical Image Fusion and Super-Resolution using Conditional Diffusion Model [2.507050016527729]
  Tri-modal medical image fusion can provide a more comprehensive view of the disease's shape, location, and biological activity. Due to the limitations of imaging equipment and considerations for patient safety, the quality of medical images is usually limited. There is an urgent need for a technology that can both enhance image resolution and integrate multi-modal information.
  arXiv  Detail & Related papers  (2024-04-26T12:13:41Z)
• Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration [54.76511683427566]
  This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
  arXiv  Detail & Related papers  (2024-02-16T05:16:20Z)
• XAI for In-hospital Mortality Prediction via Multimodal ICU Data [57.73357047856416]
  We propose an efficient, explainable AI solution for predicting in-hospital mortality via multimodal ICU data. We employ multimodal learning in our framework, which can receive heterogeneous inputs from clinical data and make decisions. Our framework can be easily transferred to other clinical tasks, which facilitates the discovery of crucial factors in healthcare research.
  arXiv  Detail & Related papers  (2023-12-29T14:28:04Z)
• Multi-modal Medical Neurological Image Fusion using Wavelet Pooled Edge Preserving Autoencoder [3.3828292731430545]
  This paper presents an end-to-end unsupervised fusion model for multimodal medical images based on an edge-preserving dense autoencoder network. In the proposed model, feature extraction is improved by using wavelet decomposition-based attention pooling of feature maps. The proposed model is trained on a variety of medical image pairs which helps in capturing the intensity distributions of the source images.
  arXiv  Detail & Related papers  (2023-10-18T11:59:35Z)
• Improving Vision Anomaly Detection with the Guidance of Language Modality [64.53005837237754]
  This paper tackles the challenges for vision modality from a multimodal point of view. We propose Cross-modal Guidance (CMG) to tackle the redundant information issue and sparse space issue. To learn a more compact latent space for the vision anomaly detector, CMLE learns a correlation structure matrix from the language modality.
  arXiv  Detail & Related papers  (2023-10-04T13:44:56Z)
• A Sequence Agnostic Multimodal Preprocessing for Clogged Blood Vessel Detection in Alzheimer's Diagnosis [1.394948342529531]
  Successful identification of blood vessel blockage is a crucial step for Alzheimer's disease diagnosis. In this study, we propose several preprocessing schemes to improve the performance of machine learning methods.
  arXiv  Detail & Related papers  (2022-11-06T13:22:05Z)
• COROLLA: An Efficient Multi-Modality Fusion Framework with Supervised Contrastive Learning for Glaucoma Grading [1.2250035750661867]
  We propose an efficient multi-modality supervised contrastive learning framework, named COROLLA, for glaucoma grading. We employ supervised contrastive learning to increase our models' discriminative power with better convergence. On the GAMMA dataset, our COROLLA framework achieves overwhelming glaucoma grading performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-01-11T06:00:51Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection [20.11589208667256]
  We propose a TSK fuzzy system-based epilepsy detection algorithm that integrates multi-view collaborative transfer learning. The proposed method has the potential to detect epileptic EEG signals effectively.
  arXiv  Detail & Related papers  (2021-11-11T12:15:55Z)
• Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso [102.84915019938413]
  Non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG) offer promise of non-invasive techniques. The problem of source localization, or source imaging, poses however a high-dimensional statistical inference challenge. We propose an ensemble of desparsified multi-task Lasso (ecd-MTLasso) to deal with this problem.
  arXiv  Detail & Related papers  (2020-09-29T21:17:16Z)
• Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion [71.87627318863612]
  We propose a novel multimodal segmentation framework which is robust to the absence of imaging modalities. Our network uses feature disentanglement to decompose the input modalities into the modality-specific appearance code. We validate our method on the important yet challenging multimodal brain tumor segmentation task with the BRATS challenge dataset.
  arXiv  Detail & Related papers  (2020-02-22T14:32:04Z)

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.