PixMed-Enhancer: An Efficient Approach for Medical Image Augmentation
AI-powered medical imaging faces persistent challenges, such as limited datasets, class imbalances, and high computational costs. To overcome these barriers, we introduce PixMed-Enhancer, a novel conditional GAN that integrates the ghost module into its encoder—a pioneering approach that achieves ef...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/12/3/235 |
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| Summary: | AI-powered medical imaging faces persistent challenges, such as limited datasets, class imbalances, and high computational costs. To overcome these barriers, we introduce PixMed-Enhancer, a novel conditional GAN that integrates the ghost module into its encoder—a pioneering approach that achieves efficient feature extraction while significantly reducing the computational complexity without compromising the performance. Our method features a hybrid loss function, uniquely combining binary cross-entropy (BCE) and a Structural Similarity Index Measure (SSIM), to ensure pixel-level precision while enhancing the perceptual realism. Additionally, the use of conditional input masks offers unparalleled control over the generation of tumor features, marking a breakthrough in fine-grained dataset augmentation for segmentation and diagnostic tasks. Rigorous testing on diverse datasets establishes PixMed-Enhancer as a state-of-the-art solution, excelling in its realism, structural fidelity, and computational efficiency. PixMed-Enhancer establishes a robust foundation for real-world clinical applications in AI-driven medical imaging. |
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| ISSN: | 2306-5354 |