Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training

• URL: http://arxiv.org/abs/2504.01547v1
• Date: Wed, 02 Apr 2025 09:41:43 GMT
• Title: Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training
• Authors: Luca Ciampi, Gabriele Lagani, Giuseppe Amato, Fabrizio Falchi,
• Abstract summary: Supervised deep learning for semantic segmentation has achieved excellent results in accurately identifying anatomical and pathological structures in medical images.<n>It often requires large annotated training datasets, which limits its scalability in clinical settings.<n>We introduce a novel semi-supervised teacher-student framework for biomedical image segmentation, inspired by the recent success of generative models.
• Score: 7.915123555266876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Supervised deep learning for semantic segmentation has achieved excellent results in accurately identifying anatomical and pathological structures in medical images. However, it often requires large annotated training datasets, which limits its scalability in clinical settings. To address this challenge, semi-supervised learning is a well-established approach that leverages both labeled and unlabeled data. In this paper, we introduce a novel semi-supervised teacher-student framework for biomedical image segmentation, inspired by the recent success of generative models. Our approach leverages denoising diffusion probabilistic models (DDPMs) to generate segmentation masks by progressively refining noisy inputs conditioned on the corresponding images. The teacher model is first trained in an unsupervised manner using a cycle-consistency constraint based on noise-corrupted image reconstruction, enabling it to generate informative semantic masks. Subsequently, the teacher is integrated into a co-training process with a twin-student network. The student learns from ground-truth labels when available and from teacher-generated pseudo-labels otherwise, while the teacher continuously improves its pseudo-labeling capabilities. Finally, to further enhance performance, we introduce a multi-round pseudo-label generation strategy that iteratively improves the pseudo-labeling process. We evaluate our approach on multiple biomedical imaging benchmarks, spanning multiple imaging modalities and segmentation tasks. Experimental results show that our method consistently outperforms state-of-the-art semi-supervised techniques, highlighting its effectiveness in scenarios with limited annotated data. The code to replicate our experiments can be found at https://github.com/ciampluca/diffusion_semi_supervised_biomedical_image_segmentation

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