Case-Aware Medical Image Classification with Multimodal Knowledge Graphs and Reliability-Guided Refinement

• URL: http://arxiv.org/abs/2605.22547v2
• Date: Wed, 27 May 2026 05:22:50 GMT
• Title: Case-Aware Medical Image Classification with Multimodal Knowledge Graphs and Reliability-Guided Refinement
• Abstract summary: We propose a case-aware reasoning framework driven by multimodal knowledge graphs for medical image classification.<n>Given an input image, our method adaptively retrieves similar cases from this memory and extracts their corresponding case-centered subgraphs.<n>Experiments on multiple medical imaging datasets demonstrate that our approach consistently outperforms strong baselines.
• Score: 27.443476988184994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has brought significant progress to medical image classification, yet most existing methods still rely on isolated visual evidence and cannot effectively leverage similar cases or external knowledge. In clinical practice, diagnosis is typically supported by similar historical cases and their associated symptoms. To explicitly model this evidence-based diagnostic process, we propose a case-aware reasoning framework driven by multimodal knowledge graphs for medical image classification. Specifically, we construct a case-aware multimodal knowledge graph as a structured diagnostic memory, where diseases, images, and symptoms are hierarchically organized. Given an input image, our method adaptively retrieves similar cases from this memory and extracts their corresponding case-centered subgraphs. We further introduce a knowledge propagation and injection mechanism, in which an image-centric Graph Attention Network aggregates heterogeneous semantics into case-based features, followed by a bidirectional cross-modal attention mechanism that injects these features into visual representations for cross-modal alignment. To mitigate noisy retrieval, we design a confidence-calibrated decision refinement scheme that estimates the reliability of each retrieved case by jointly considering prediction confidence and sample similarity, and reweights its contribution to the final prediction, providing interpretable case-level evidence. Extensive experiments on multiple medical imaging datasets demonstrate that our approach consistently outperforms strong baselines, while ablation and qualitative analyses validate its effectiveness and interpretability. The code is available at https://anonymous.4open.science/r/MKG-CARE-8B7B.

Related papers

• Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models [56.29123284262618]
  This work proposes leveraging the universal representation capabilities of visual foundation models to estimate inherent data uncertainty.<n>We analyze the feature diversity of the model's decoded representations and quantify their singular value energy to define the semantic perception scale for each class.<n>Based on this foundation, we design two uncertainty-driven application strategies: (1) the aleatoric uncertainty-aware data filtering mechanism to eliminate potentially noisy samples and enhance model learning quality; and (2) the dynamic uncertainty-aware optimization strategy that adaptively adjusts class-specific loss weights during training based on the semantic perception scale, combined with a label denoising mechanism to improve training stability
  arXiv  Detail & Related papers  (2026-04-13T03:59:54Z)
• Clinical Cognition Alignment for Gastrointestinal Diagnosis with Multimodal LLMs [63.535652574541764]
  Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in medical image analysis.<n>Their application in gastrointestinal endoscopy is currently hindered by two critical limitations.<n>We propose a novel Clinical-Cognitive-Aligned (CogAlign) framework to address these challenges.
  arXiv  Detail & Related papers  (2026-03-21T07:47:37Z)
• UbiQVision: Quantifying Uncertainty in XAI for Image Recognition [39.47298454012977]
  SHAP explanations can be unstable and unreliable in the presence of epistemic and aleatoric uncertainty.<n>This study uses Dirichlet posterior sampling and Dempster-Shafer theory to quantify the uncertainty that arises from these unstable explanations in medical imaging applications.
  arXiv  Detail & Related papers  (2025-12-23T11:57:34Z)
• RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosis [56.373297358647655]
  Retrieval-Augmented Diagnosis (RAD) is a novel framework that injects external knowledge into multimodal models directly on downstream tasks.<n>RAD operates through three key mechanisms: retrieval and refinement of disease-centered knowledge from multiple medical sources, a guideline-enhanced contrastive loss transformer, and a dual decoder.
  arXiv  Detail & Related papers  (2025-09-24T10:36:14Z)
• Multimodal Medical Image Classification via Synergistic Learning Pre-training [20.818508328120974]
  We propose a novel framework for multimodal semi-supervised medical image classification.<n>By treating one modality as an augmented sample of another modality, we implement a self-supervised learning pre-train.<n>During the fine-tuning stage, we set different encoders to extract features from the original modalities.
  arXiv  Detail & Related papers  (2025-09-22T08:21:19Z)
• A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis [48.84443450990355]
  Deep networks have achieved broad success in analyzing natural images, when applied to medical scans, they often fail in unexcepted situations. We investigate this challenge and focus on model sensitivity to domain shifts, such as data sampled from different hospitals or data confounded by demographic variables such as sex, race, etc, in the context of chest X-rays and skin lesion images. Taking inspiration from medical training, we propose giving deep networks a prior grounded in explicit medical knowledge communicated in natural language.
  arXiv  Detail & Related papers  (2024-05-23T17:55:02Z)
• VALD-MD: Visual Attribution via Latent Diffusion for Medical Diagnostics [0.0]
  Visual attribution in medical imaging seeks to make evident the diagnostically-relevant components of a medical image. We here present a novel generative visual attribution technique, one that leverages latent diffusion models in combination with domain-specific large language models. The resulting system also exhibits a range of latent capabilities including zero-shot localized disease induction.
  arXiv  Detail & Related papers  (2024-01-02T19:51:49Z)
• 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)
• Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI [1.049712834719005]
  We present a novel method to automatically classify medical images that learns and leverages weak causal signals in the image. Our framework consists of a convolutional neural network backbone and a causality-extractor module. Our findings show that causal relationships among features play a crucial role in enhancing the model's ability to discern relevant information.
  arXiv  Detail & Related papers  (2023-09-19T16:08:33Z)
• Multimorbidity Content-Based Medical Image Retrieval Using Proxies [37.47987844057842]
  We propose a novel multi-label metric learning method that can be used for both classification and content-based image retrieval. Our model is able to support diagnosis by predicting the presence of diseases and provide evidence for these predictions. We demonstrate the efficacy of our approach to both classification and content-based image retrieval on two multimorbidity radiology datasets.
  arXiv  Detail & Related papers  (2022-11-22T11:23:53Z)
• 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)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)

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.