mlsanityData to 14 Oct 2026

Enriched text-guided variational multimodal knowledge distillation network (VMD) for automated diagnosis of plaque vulnerability in 3D carotid artery MRI

arXiv 2509.11924 ↗First posted 15 Sep 2025cs.CV cs.LGcited by 0

On the map: Knowledge distillation → Distillation for medical imaging

Abstract (arXiv)

Multimodal learning has attracted much attention in recent years due to its ability to effectively utilize data features from a variety of different modalities. Diagnosing the vulnerability of atherosclerotic plaques directly from carotid 3D MRI images is relatively challenging for both radiologists and conventional 3D vision networks. In clinical practice, radiologists assess patient conditions using a multimodal approach that incorporates various imaging modalities and domain-specific expertise, paving the way for the creation of multimodal diagnostic networks. In this paper, we have developed an effective strategy to leverage radiologists' domain knowledge to automate the diagnosis of carotid plaque vulnerability through Variation inference and Multimodal knowledge Distillation (VMD). This method excels in harnessing cross-modality prior knowledge from limited image annotations and radiology reports within training data, thereby enhancing the diagnostic network's accuracy for unannotated 3D MRI images. We conducted in-depth experiments on the dataset collected in-house and verified the effectiveness of the VMD strategy we proposed.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation. Builds on 20 of its 41 references that are in the map. First posted on arXiv 15 Sep 2025.

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20 of 41 references.

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Same direction, newest first

  1. 2021Variational Knowledge Distillation for Disease Classification in Chest X-Rays19 Mar

Other references · 19

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
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  10. 2019ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmissioncited by 493
  11. 2019Variational Information Distillation for Knowledge Transfercited by 324
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7 more
  1. 2020A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promisescited by 231
  2. 2022LinkBERT: Pretraining Language Models with Document Linkscited by 211
  3. 2021Distilling Audio-Visual Knowledge by Compositional Contrastive Learningcited by 53
  4. 2023Text-guided Foundation Model Adaptation for Pathological Image Classificationcited by 47
  5. 2020Conceptualized Representation Learning for Chinese Biomedical Text Miningcited by 19
  6. 2020MC-BERT: Efficient Language Pre-Training via a Meta Controllercited by 15
  7. 2023Enhanced Multimodal Representation Learning with Cross-modal KDcited by 11

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