Abstract
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 prac-
tice, radiologists assess patient conditions using a multimodal approach
that incorporates various imaging modalities and domain-specific exper-
tise, paving the way for the creation of multimodal diagnostic networks.
In this paper, we have developed an effective strategy to leverage radi-
ologists’ 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 accu-
racy for unannotated 3D MRI images. We conducted in-depth experi-
ments on the dataset collected in-house and verified the effectiveness of
the VMD strategy we proposed. Code will be available at this url.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/ADSMI2024_012.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{CaoBo_Variational_MICCAISAT2026,
author = { Cao, Bo AND Feng, Mengmeng AND Yu, Fan AND Qian, Zhen AND Lu, Jie},
title = { { Variational multimodal distillation for diagnosing plaque vulnerability in carotid 3D MRI } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17276},
month = {pending},
page = {pending}
}