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New dataset GingivitisSeg benchmarks AI for dental inflammation detection

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced GingivitisSeg, a new public dataset and benchmark designed for segmenting gingivitis in intraoral images. The dataset includes 961 images with pixel-level annotations, split into training, validation, and test sets. Seven different semantic segmentation model configurations were evaluated, with UPerNet using ResNet50 achieving the highest mean Intersection over Union (mIoU). However, the performance differences between the top models were not statistically significant after applying Holm correction. AI

IMPACT This dataset and benchmark could advance AI applications in dental diagnostics by improving the accuracy of gingivitis detection.

RANK_REASON The cluster describes a new dataset and benchmark for a specific AI task, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset GingivitisSeg benchmarks AI for dental inflammation detection

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The cluster describes a new dataset and benchmark for a specific AI task, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zeynep Ozdemir, Zehra Hasgul, Irem Ulku, Omer Ozgur Tanriover, Fatma Karacaoglu, Murat Akkaya ·

    GingivitisSeg: A Public Dataset and Comparative Benchmark for Gingivitis Segmentation in Intraoral Images

    arXiv:2611.15184v1 Announce Type: cross Abstract: Gingivitis segmentation from intraoral photographs can support computer-assisted periodontal assessment, but pixel-level delineation remains challenging due to severe foreground-background class imbalance, low-contrast boundaries,…

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