.png){kind=icon}
.png){kind=icon}
Return
Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification
.png){kind=icon}
PRE
AI
DOI：10.1007/s11263-026-02786-3.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In the medical field, the limited availability of large-scale datasets and labor-intensive annotation processes hinder the performance of deep models. Diffusion-based generative augmentation approaches present a promising solution to this issue, having been proven effective in advancing downstream medical recognition tasks. Nevertheless, existing works lack sufficient semantic and sequential steerability for challenging video/3D sequence generation, and neglect quality control of noisy synthesized samples, resulting in unreliable synthetic databases and severely limiting the performance of downstream tasks. In this work, we present Ctrl-GenAug, a novel and general generative augmentation framework that enables highly semantic- and sequential-customized sequence synthesis and suppresses incorrectly synthesized samples, to aid medical sequence classification. Specifically, we first design a multimodal conditions-guided sequence generator for controllably synthesizing diagnosis-promotive samples. A sequential augmentation module is integrated to enhance the temporal/stereoscopic coherence of generated samples. Then, we propose a noisy synthetic data filter to suppress unreliable cases at the semantic and sequential levels. Extensive experiments on 5 medical datasets with 4 different modalities, including comparisons with 15 augmentation methods and evaluations using 11 networks trained on 3 paradigms, comprehensively demonstrate the effectiveness and generality of Ctrl-GenAug, particularly with pronounced performance gains in underrepresented high-risk populations and out-domain conditions. Codes, models, and synthetic databases are available at https://github.com/XinRuiZhou0106/Ctrl-GenAug .
Keywords:
Classification
Controllable generative augmentation
Medical sequence synthesis
Diffusion model
Journal
IF:
9.3
Papers:
3.9K
Citations:
2.8W
View more organizations