Papers › MedITok: A Unified Tokenizer for Medical Image Synthesis and Interpretation

MedITok: A Unified Tokenizer for Medical Image Synthesis and Interpretation

25 May 2025arXiv:2505.19225archive 2025-07-28

Chenglong Ma, Yuanfeng Ji, Jin Ye, Zilong Li, Chenhui Wang, Junzhi Ning, Wei Li, Lihao Liu, Qiushan Guo, Tianbin Li, Junjun He, Hongming Shan

Advanced autoregressive models have reshaped multimodal AI. However, their transformative potential in medical imaging remains largely untapped due to the absence of a unified visual tokenizer -- one capable of capturing fine-grained visual structures for faithful image reconstruction and realistic image synthesis, as well as rich semantics for accurate diagnosis and image interpretation. To this end, we present MedITok, the first unified tokenizer tailored for medical images, encoding both low-level structural details and high-level clinical semantics within a unified latent space. To balance these competing objectives, we introduce a novel two-stage training framework: a visual representation alignment stage that cold-starts the tokenizer reconstruction learning with a visual semantic constraint, followed by a textual semantic representation alignment stage that infuses detailed clinical semantics into the latent space. Trained on the meticulously collected large-scale dataset with over 30 million medical images and 2 million image-caption pairs, MedITok achieves state-of-the-art performance on more than 30 datasets across 9 imaging modalities and 4 different tasks. By providing a unified token space for autoregressive modeling, MedITok supports a wide range of tasks in clinical diagnostics and generative healthcare applications. Model and code will be made publicly available at: https://github.com/Masaaki-75/meditok.

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drop_path masaaki-75/meditok/layers/drop_path.py official repository ran · fixture could not drive it MIT (permissive) · c157f5b112b3a392 · report
make_2tuple masaaki-75/meditok/layers/patch_embed.py official repository ran · honoured contract fingerprinted MIT (permissive) · 3c04490a215299f2 · report
add_residual masaaki-75/meditok/layers/block.py official repository unverified MIT (permissive) · 5d16d4d4fc573ac7 · report
check_file_ok masaaki-75/meditok/batch_infer.py official repository unverified MIT (permissive) · a9fc31a45465d6c8 · report
drop_add_residual_stochastic_depth masaaki-75/meditok/layers/block.py official repository unverified MIT (permissive) · 85f7ffc01945bb72 · report
get_branges_scales masaaki-75/meditok/layers/block.py official repository unverified MIT (permissive) · 5be3610fa1fee19e · report
get_meditok_args masaaki-75/meditok/models/meditok.py official repository unverified MIT (permissive) · 9cfe67ef3f283984 · report
prepare_inputs_for_generation masaaki-75/meditok/local_openclip/coca_model.py official repository unverified MIT (permissive) · fb651d0a97fd4d3f · report
register_pooler masaaki-75/meditok/local_openclip/hf_model.py official repository unverified MIT (permissive) · 2a377da4a76a2d44 · report
stable_linear_transform masaaki-75/meditok/batch_infer.py official repository unverified MIT (permissive) · e814250e8fac9df2 · report

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