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<p>Public food image datasets have primarily focused on recognition or segmentation, with limited resources for comprehensive nutrition analysis, particularly for Chinese cuisine which exhibits high nutritional variability due to diverse cooking methods and regional influences. To address this gap, we introduce a multimodal food image dataset designed to support full-cycle nutritional analysis, including segmentation, category recognition, and nutrient content estimation, tailored specifically to diabetic diets. This dataset is aligned with clinical data from Chinese diabetes cohorts to facilitate accurate dietary management and glucose prediction. It provides a valuable resource for advancing computational methods in meal-level nutrition assessment for diabetic populations.</p>

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Chinese Food Images for Full-cycle Nutrition Analysis Towards Diabetes Management

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月29日

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  • Yuanxin Jin,
  • Ming Li,
  • Qinpei Zhao,
  • Chenwei Wu,
  • Shuang Liu,
  • Lu Yao,
  • Jessie Chen,
  • Jiangfeng Li,
  • Weixiong Rao,
  • Juliang Xu,
  • Congrong Wang

摘要

Public food image datasets have primarily focused on recognition or segmentation, with limited resources for comprehensive nutrition analysis, particularly for Chinese cuisine which exhibits high nutritional variability due to diverse cooking methods and regional influences. To address this gap, we introduce a multimodal food image dataset designed to support full-cycle nutritional analysis, including segmentation, category recognition, and nutrient content estimation, tailored specifically to diabetic diets. This dataset is aligned with clinical data from Chinese diabetes cohorts to facilitate accurate dietary management and glucose prediction. It provides a valuable resource for advancing computational methods in meal-level nutrition assessment for diabetic populations.