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| Natura: | Preprint |
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2025
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| Accesso online: | https://arxiv.org/abs/2511.04811 |
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| _version_ | 1866917067287953408 |
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| author | Zhao, Shuo Zhou, Yu Chen, Jianxu |
| author_facet | Zhao, Shuo Zhou, Yu Chen, Jianxu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04811 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Active Learning Pipeline for Biomedical Image Instance Segmentation with Minimal Human Intervention Zhao, Shuo Zhou, Yu Chen, Jianxu Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 68T07, 68U10 I.2.10; I.4.6; J.3 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. |
| title | An Active Learning Pipeline for Biomedical Image Instance Segmentation with Minimal Human Intervention |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 68T07, 68U10 I.2.10; I.4.6; J.3 |
| url | https://arxiv.org/abs/2511.04811 |