Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification

• URL: http://arxiv.org/abs/2409.17091v1
• Date: Wed, 25 Sep 2024 16:58:19 GMT
• Title: Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification
• Authors: Xinrui Zhou, Yuhao Huang, Haoran Dou, Shijing Chen, Ao Chang, Jia Liu, Weiran Long, Jian Zheng, Erjiao Xu, Jie Ren, Ruobing Huang, Jun Cheng, Wufeng Xue, Dong Ni,
• Abstract summary: Ctrl-GenAug is a novel and general generative augmentation framework. It enables highly semantic- and sequential-customized sequence synthesis and suppresses incorrectly synthesized samples.
• Score: 16.02675888386905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 semantic and sequential levels. Extensive experiments on 3 medical datasets, using 11 networks trained on 3 paradigms, comprehensively analyze the effectiveness and generality of Ctrl-GenAug, particularly in underrepresented high-risk populations and out-domain conditions.

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