Shared Semantic Codebook Distillation for Unpaired Cross-Modal Medical Classification
Cross-modal knowledge distillation often fails when medical image modalities are unpaired. This paper introduces Shared Semantic Codebook Distillation (SSCD), which uses a shared discrete codebook to represent images as distributions and aligns teacher and student modalities globally and per class—without paired samples or directly comparable features. SSCD improves student macro-F1 to 70.2 and 76.3 on two unpaired tasks and outperforms existing distillation baselines.
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[Submitted on 29 Jul 2026]
Title:Shared Semantic Codebook Distillation for Unpaired Cross-Modal Medical Classification
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Abstract:Cross-modal knowledge distillation can transfer diagnostic knowledge from a strong but costly teacher modality to a cheaper and more deployable student modality. In medical image analysis, however, the two modalities are often unpaired: they are collected from different patient cohorts and occupy geometrically incompatible feature spaces. This makes instance-level distillation invalid and direct feature matching unreliable. To address these challenges, we propose Shared Semantic Codebook Distillation (SSCD), which compares teacher and student representations through a shared discrete codebook. Each image is represented as a distribution over a common, modality-agnostic vocabulary, and knowledge is transferred by aligning these distributions across modalities, both globally and class-conditionally, without requiring paired samples or directly comparable raw features. The codebook is evolved online by exponential moving average and kept diverse through entropy regularization and dead-code restart. At inference, all teacher-side and codebook modules are discarded, leaving only the student encoder and classifier. On two heterogeneous unpaired settings, OCT-to-fundus retinal disease classification and CT-to-chest-X-ray pneumonia classification, SSCD improves the student from 64.5 to 70.2 macro-F1 and from 73.8 to 76.3 macro-F1, respectively, outperforming all evaluated distillation baselines on both settings. Code and pretrained models are available at this https URL
Comments: 16 pages, 2 figures, 4 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
Cite as: arXiv:2607.27357 [cs.CV]
(or arXiv:2607.27357v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.27357
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Dillan Imans [view email] [v1] Wed, 29 Jul 2026 18:10:50 UTC (4,720 KB)
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