Robust Multimodal Learning for Ophthalmic Disease Grading via Disentangled Representation

• URL: http://arxiv.org/abs/2503.05319v1
• Date: Fri, 07 Mar 2025 10:58:38 GMT
• Title: Robust Multimodal Learning for Ophthalmic Disease Grading via Disentangled Representation
• Authors: Xinkun Wang, Yifang Wang, Senwei Liang, Feilong Tang, Chengzhi Liu, Ming Hu, Chao Hu, Junjun He, Zongyuan Ge, Imran Razzak,
• Abstract summary: multimodal data is rare in real-world applications due to a lack of medical equipment and concerns about data privacy.<n>Traditional deep learning methods typically address these issues by learning representations in latent space.<n>Authors propose the Essence-Point and Disentangle Representation Learning (EDRL) strategy, which integrates a self-distillation mechanism into an end-to-end framework.
• Score: 30.697291934309206
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper discusses how ophthalmologists often rely on multimodal data to improve diagnostic accuracy. However, complete multimodal data is rare in real-world applications due to a lack of medical equipment and concerns about data privacy. Traditional deep learning methods typically address these issues by learning representations in latent space. However, the paper highlights two key limitations of these approaches: (i) Task-irrelevant redundant information (e.g., numerous slices) in complex modalities leads to significant redundancy in latent space representations. (ii) Overlapping multimodal representations make it difficult to extract unique features for each modality. To overcome these challenges, the authors propose the Essence-Point and Disentangle Representation Learning (EDRL) strategy, which integrates a self-distillation mechanism into an end-to-end framework to enhance feature selection and disentanglement for more robust multimodal learning. Specifically, the Essence-Point Representation Learning module selects discriminative features that improve disease grading performance. The Disentangled Representation Learning module separates multimodal data into modality-common and modality-unique representations, reducing feature entanglement and enhancing both robustness and interpretability in ophthalmic disease diagnosis. Experiments on multimodal ophthalmology datasets show that the proposed EDRL strategy significantly outperforms current state-of-the-art methods.

Related papers

• Continual Multimodal Contrastive Learning [70.60542106731813]
  Multimodal contrastive learning (MCL) advances in aligning different modalities and generating multimodal representations in a joint space. However, a critical yet often overlooked challenge remains: multimodal data is rarely collected in a single process, and training from scratch is computationally expensive. In this paper, we formulate CMCL through two specialized principles of stability and plasticity. We theoretically derive a novel optimization-based method, which projects updated gradients from dual sides onto subspaces where any gradient is prevented from interfering with the previously learned knowledge.
  arXiv  Detail & Related papers  (2025-03-19T07:57:08Z)
• MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention [52.106879463828044]
  Histopathology and transcriptomics are fundamental modalities in oncology, encapsulating the morphological and molecular aspects of the disease.<n>We present MIRROR, a novel multi-modal representation learning method designed to foster both modality alignment and retention.<n>Extensive evaluations on TCGA cohorts for cancer subtyping and survival analysis highlight MIRROR's superior performance.
  arXiv  Detail & Related papers  (2025-03-01T07:02:30Z)
• Incomplete Modality Disentangled Representation for Ophthalmic Disease Grading and Diagnosis [16.95583564875497]
  We propose an Incomplete Modality Disentangled Representation (IMDR) strategy to disentangle features into explicit independent modal-common and modal-specific features.<n> Experiments on four multimodal datasets demonstrate that the proposed IMDR outperforms the state-of-the-art methods significantly.
  arXiv  Detail & Related papers  (2025-02-17T12:10:35Z)
• MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks [50.98856172702256]
  We propose the Modality-INformed knowledge Distillation (MIND) framework, a multimodal model compression approach.<n>MIND transfers knowledge from ensembles of pre-trained deep neural networks of varying sizes into a smaller multimodal student.<n>We evaluate MIND on binary and multilabel clinical prediction tasks using time series data and chest X-ray images.
  arXiv  Detail & Related papers  (2025-02-03T08:50:00Z)
• A Learnable Multi-views Contrastive Framework with Reconstruction Discrepancy for Medical Time-Series [8.741139851597364]
  We propose incorporating external data from related tasks and leveraging AE-GAN to extract prior knowledge. We introduce LMCF, a framework that integrates a multi-head attention mechanism and adaptively learns representations from different views. Experiments on three target datasets demonstrate that our method consistently outperforms seven other baselines.
  arXiv  Detail & Related papers  (2025-01-30T14:20:11Z)
• Multimodal Fusion Learning with Dual Attention for Medical Imaging [8.74917075651321]
  Multimodal fusion learning has shown significant promise in classifying various diseases such as skin cancer and brain tumors.<n>Existing methods face three key limitations.<n>DRIFA can be integrated with any deep neural network, forming a multimodal fusion learning framework denoted as DRIFA-Net.
  arXiv  Detail & Related papers  (2024-12-02T08:11:12Z)
• Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework [58.362064122489166]
  This paper introduces the Cross-modal Few-Shot Learning task, which aims to recognize instances across multiple modalities while relying on scarce labeled data. We propose a Generative Transfer Learning framework by simulating how humans abstract and generalize concepts. We show that the GTL achieves state-of-the-art performance across seven multi-modal datasets across RGB-Sketch, RGB-Infrared, and RGB-Depth.
  arXiv  Detail & Related papers  (2024-10-14T16:09:38Z)
• ViKL: A Mammography Interpretation Framework via Multimodal Aggregation of Visual-knowledge-linguistic Features [54.37042005469384]
  We announce MVKL, the first multimodal mammography dataset encompassing multi-view images, detailed manifestations and reports. Based on this dataset, we focus on the challanging task of unsupervised pretraining. We propose ViKL, a framework that synergizes Visual, Knowledge, and Linguistic features.
  arXiv  Detail & Related papers  (2024-09-24T05:01:23Z)
• Multi-OCT-SelfNet: Integrating Self-Supervised Learning with Multi-Source Data Fusion for Enhanced Multi-Class Retinal Disease Classification [2.5091334993691206]
  Development of a robust deep-learning model for retinal disease diagnosis requires a substantial dataset for training. The capacity to generalize effectively on smaller datasets remains a persistent challenge. We've combined a wide range of data sources to improve performance and generalization to new data.
  arXiv  Detail & Related papers  (2024-09-17T17:22:35Z)
• HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data [10.774128925670183]
  This paper presents the Hybrid Early-fusion Attention Learning Network (HEALNet), a flexible multimodal fusion architecture. We conduct multimodal survival analysis on Whole Slide Images and Multi-omic data on four cancer datasets from The Cancer Genome Atlas (TCGA) HEALNet achieves state-of-the-art performance compared to other end-to-end trained fusion models.
  arXiv  Detail & Related papers  (2023-11-15T17:06:26Z)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)
• 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)
• MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data [75.73881040581767]
  We propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations. Our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
  arXiv  Detail & Related papers  (2020-02-09T14:11:50Z)

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.