Synthetic data generation with Worley-Perlin diffusion for robust subarachnoid hemorrhage detection in imbalanced CT Datasets.
Authors
Affiliations (8)
Affiliations (8)
- Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan. [email protected].
- Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan.
- Information Technology Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan.
- Department of Neurosurgery, Graduate School of Medicine, Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, Japan.
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan.
- Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan. [email protected].
- Information Technology Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan. [email protected].
- Research Center for Medical Bigdata, National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan. [email protected].
Abstract
In this paper, we propose a novel generative model to produce high-quality SAH samples, enhancing SAH CT detection performance in imbalanced datasets. Previous methods, such as cost-sensitive learning and previous diffusion models, suffer from overfitting or noise-induced distortion, limiting their effectiveness. Accurate SAH sample generation is crucial for better detection. We propose the Worley-Perlin Diffusion Model (WPDM), leveraging Worley-Perlin noise to synthesize diverse, high-quality SAH images. WPDM addresses limitations of Gaussian noise (homogeneity) and Simplex noise (distortion), enhancing robustness for generating SAH images. Additionally, <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mtext>WPDM</mtext> <mtext>Fast</mtext></msub> </math> optimizes generation speed without compromising quality. WPDM effectively improved classification accuracy in datasets with varying imbalance ratios. Notably, a classifier trained with WPDM-generated samples achieved an F1-score of 0.857 on a 1:36 imbalance ratio, surpassing the state of the art by 2.3 percentage points. WPDM overcomes the limitations of Gaussian and Simplex noise-based models, generating high-quality, realistic SAH images. It significantly enhances classification performance in imbalanced settings, providing a robust solution for SAH CT detection.