An Active Learning Pipeline for Biomedical Image Instance Segmentation with Minimal Human Intervention

• URL: http://arxiv.org/abs/2511.04811v1
• Date: Thu, 06 Nov 2025 21:07:26 GMT
• Title: An Active Learning Pipeline for Biomedical Image Instance Segmentation with Minimal Human Intervention
• Authors: Shuo Zhao, Yu Zhou, Jianxu Chen,
• Abstract summary: Deep learning models such as U-Net have set new benchmarks in segmentation performance.<n>nnU-Net requires a substantial amount of annotated data for cross-validation.<n>This work proposes a data-centric AI workflow that leverages active learning and pseudo-labeling.
• Score: 4.805039406228118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biomedical image segmentation is critical for precise structure delineation and downstream analysis. Traditional methods often struggle with noisy data, while deep learning models such as U-Net have set new benchmarks in segmentation performance. nnU-Net further automates model configuration, making it adaptable across datasets without extensive tuning. However, it requires a substantial amount of annotated data for cross-validation, posing a challenge when only raw images but no labels are available. Large foundation models offer zero-shot generalizability, but may underperform on specific datasets with unique characteristics, limiting their direct use for analysis. This work addresses these bottlenecks by proposing a data-centric AI workflow that leverages active learning and pseudo-labeling to combine the strengths of traditional neural networks and large foundation models while minimizing human intervention. The pipeline starts by generating pseudo-labels from a foundation model, which are then used for nnU-Net's self-configuration. Subsequently, a representative core-set is selected for minimal manual annotation, enabling effective fine-tuning of the nnU-Net model. This approach significantly reduces the need for manual annotations while maintaining competitive performance, providing an accessible solution for biomedical researchers to apply state-of-the-art AI techniques in their segmentation tasks. The code is available at https://github.com/MMV-Lab/AL_BioMed_img_seg.

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