Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification
Journal:
arXiv
Published Date:
Sep 25, 2024
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.
