Summary of Ctrl-genaug: Controllable Generative Augmentation For Medical Sequence Classification, by Xinrui Zhou et al.
Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification
by 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
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Ctrl-GenAug, a novel generative augmentation framework designed to address the limitations of existing diffusion-based approaches in medical sequence generation. The proposed method enables semantic- and sequential-customized synthesis and suppresses noisy samples, leading to improved performance in downstream tasks such as medical sequence classification. The authors develop a multimodal conditions-guided sequence generator for controllably synthesizing diagnosis-promotive samples and integrate a sequential augmentation module to enhance temporal/stereoscopic coherence. Additionally, they propose a noisy synthetic data filter to suppress unreliable cases at semantic and sequential levels. Experimental results on three medical datasets using 11 networks trained on three paradigms demonstrate the effectiveness and generality of Ctrl-GenAug, particularly in underrepresented high-risk populations and out-domain conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make fake medical data that’s really good at helping doctors diagnose patients. Right now, making fake medical data is hard because it takes a lot of work and the results aren’t always great. The authors came up with a solution called Ctrl-GenAug that can create fake data that looks like real medical data and helps doctors get better diagnoses. They tested their idea on three different sets of medical data and used 11 different computer programs to see if it worked well in different situations. It did! This new way of making fake medical data could help doctors diagnose patients more accurately, especially for people who are at high risk or have unusual symptoms. |
Keywords
» Artificial intelligence » Classification » Diffusion » Synthetic data
