VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation
Consistency learning with feature perturbation is a widely used strategy in semi-supervised medical image segmentation. However, many existing perturbation methods rely on dropout, and thus require a careful manual tuning of the dropout rate, which is a sensitive hyperparameter and often difficult to optimize and may lead to suboptimal regularization. To overcome this limitation, we propose VQ-Seg, the first approach to employ vector quantization (VQ) to discretize the feature space and introduce a novel and controllable Quantized Perturbation Module (QPM) that replaces dropout. Our QPM perturbs discrete representations by shuffling the spatial locations of codebook indices, enabling effective and controllable regularization. To mitigate potential information loss caused by quantization, we design a dual-branch architecture where the post-quantization feature space is shared by both image reconstruction and segmentation tasks. Moreover, we introduce a Post-VQ Feature Adapter (PFA) to incorporate guidance from a foundation model (FM), supplementing the high-level semantic information lost during quantization. Furthermore, we collect a large-scale Lung Cancer (LC) dataset comprising 828 CT scans annotated for central-type lung carcinoma. Extensive experiments on the LC dataset and other public benchmarks demonstrate the effectiveness of our method, which outperforms state-of-the-art approaches. Code available at: https://github.com/script-Yang/VQ-Seg.
696a120e844a787c4fdea48b
Sicheng Yang
claimed_verified
2026-01-16T10:28:07.506Z
64b047abb02b95456db10915
/avatars/1973377424ff5d586027b91c32f8675c.svg
David Yang
yscript
user
yscript
696a120e844a787c4fdea48c
Zhaohu Xing
admin_assigned
2026-01-16T15:41:24.661Z
632c2100ea6e62428ab201e9
/avatars/5bd355e095af93261928198e3d6d5696.svg
Xingzhaohu
xingzhaohu
user
xingzhaohu
696a120e844a787c4fdea48d
Lei Zhu
VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation
Consistency learning with feature perturbation is a widely used strategy in semi-supervised medical image segmentation. However, many existing perturbation methods rely on dropout, and thus require a careful manual tuning of the dropout rate, which is a sensitive hyperparameter and often difficult to optimize and may lead to suboptimal regularization. To overcome this limitation, we propose VQ-Seg, the first approach to employ vector quantization (VQ) to discretize the feature space and introduce a novel and controllable Quantized Perturbation Module (QPM) that replaces dropout. Our QPM perturbs discrete representations by shuffling the spatial locations of codebook indices, enabling effective and controllable regularization. To mitigate potential information loss caused by quantization, we design a dual-branch architecture where the post-quantization feature space is shared by both image reconstruction and segmentation tasks. Moreover, we introduce a Post-VQ Feature Adapter (PFA) to incorporate guidance from a foundation model (FM), supplementing the high-level semantic information lost during quantization. Furthermore, we collect a large-scale Lung Cancer (LC) dataset comprising 828 CT scans annotated for central-type lung carcinoma. Extensive experiments on the LC dataset and other public benchmarks demonstrate the effectiveness of our method, which outperforms state-of-the-art approaches. Code available at: https://github.com/script-Yang/VQ-Seg.
VQ-Seg introduces a vector quantization-based perturbation method for medical image segmentation that replaces dropout with a controllable quantized perturbation module while maintaining performance through a dual-branch architecture and foundation model guidance.
vector quantization feature perturbation consistency learning semi-supervised medical image segmentation dropout Quantized Perturbation Module dual-branch architecture foundation model Post-VQ Feature Adapter