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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.04154 |
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| _version_ | 1866912875050696704 |
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| author | Lu, Mingjian Tripathi, Pawan K. Shteyn, Mark Ganguly, Debargha French, Roger H. Chaudhary, Vipin Wu, Yinghui |
| author_facet | Lu, Mingjian Tripathi, Pawan K. Shteyn, Mark Ganguly, Debargha French, Roger H. Chaudhary, Vipin Wu, Yinghui |
| contents | Segmentation architectures are typically benchmarked on single imaging modalities, obscuring deployment-relevant performance variations: an architecture optimal for one modality may underperform on another. We present a cross-modal evaluation framework for materials image segmentation spanning SEM, AFM, XCT, and optical microscopy. Our evaluation of six encoder-decoder combinations across seven datasets reveals that optimal architectures vary systematically by context: UNet excels for high-contrast 2D imaging while DeepLabv3+ is preferred for the hardest cases. The framework also provides deployment feedback via out-of-distribution detection and counterfactual explanations that reveal which microstructural features drive predictions. Together, the architecture guidance, reliability signals, and interpretability tools address a practical gap in materials characterization, where researchers lack tools to select architectures for their specific imaging setup or assess when models can be trusted on new samples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_04154 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Context Determines Optimal Architecture in Materials Segmentation Lu, Mingjian Tripathi, Pawan K. Shteyn, Mark Ganguly, Debargha French, Roger H. Chaudhary, Vipin Wu, Yinghui Computer Vision and Pattern Recognition Segmentation architectures are typically benchmarked on single imaging modalities, obscuring deployment-relevant performance variations: an architecture optimal for one modality may underperform on another. We present a cross-modal evaluation framework for materials image segmentation spanning SEM, AFM, XCT, and optical microscopy. Our evaluation of six encoder-decoder combinations across seven datasets reveals that optimal architectures vary systematically by context: UNet excels for high-contrast 2D imaging while DeepLabv3+ is preferred for the hardest cases. The framework also provides deployment feedback via out-of-distribution detection and counterfactual explanations that reveal which microstructural features drive predictions. Together, the architecture guidance, reliability signals, and interpretability tools address a practical gap in materials characterization, where researchers lack tools to select architectures for their specific imaging setup or assess when models can be trusted on new samples. |
| title | Context Determines Optimal Architecture in Materials Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.04154 |