Towards Data-Efficient Medical Imaging: A Generative and Semi-Supervised Framework
Deep learning in medical imaging is often limited by scarce and imbalanced annotated data. We present SSGNet, a unified framework that combines class specific generative modeling with iterative semisupervised pseudo labeling to enhance both classification and segmentation. Rather than functioning as a standalone model, SSGNet augments existing baselines by expanding training data with StyleGAN3 generated images and refining labels through iterative pseudo labeling. Experiments across multiple medical imaging benchmarks demonstrate consistent gains in classification and segmentation performance, while Frechet Inception Distance analysis confirms the high quality of generated samples. These results highlight SSGNet as a practical strategy to mitigate annotation bottlenecks and improve robustness in medical image analysis.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
3N-GAN: Semi-Supervised Classification of X-Ray Images with a 3-Player Adversarial Framework
The success of deep learning for medical imaging tasks, such as classification, is heavily reliant on the availability of large-scale datasets. However, acquiring datasets with large quantities of labeled data is challen…
ClassificationTricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priors
Medical image segmentation is routinely performed to isolate regions of interest, such as organs and lesions. Currently, deep learning is the state of the art for automatic segmentation, but is usually limited by the nee…
Image GenerationImage SegmentationMedical Image SegmentationSegmentation+2Unsupervised learning for concept detection in medical images: a comparative analysis
As digital medical imaging becomes more prevalent and archives increase in size, representation learning exposes an interesting opportunity for enhanced medical decision support systems. On the other hand, medical imagin…
Information RetrievalRepresentation LearningRetrievalNot-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentio…
BIG-bench Machine LearningMedical Image AnalysisTransfer LearningAnalysing the effectiveness of a generative model for semi-supervised medical image segmentation
Image segmentation is important in medical imaging, providing valuable, quantitative information for clinical decision-making in diagnosis, therapy, and intervention. The state-of-the-art in automated segmentation remain…
Decision MakingImage SegmentationMedical Image SegmentationSegmentation+2