ArticleScientific reports2025
Deliod a lightweight detection model for intestinal organoids based on deep learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Classification of different light colors applied during the incubation period based on small intestine morphology with XGBoost algorithm.BMC veterinary research · 2026Article
- Integrating Human Intestinal Organoids into FDA's New Approach Methodologies for Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Advances in organoid imaging and automated morphometric analysis: from optical microscopy to computational approaches.Frontiers in cell and developmental biology · 2026Review
- Iteration of Tumor Organoids in Drug Development: Simplification and Integration.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Immunomodulatory Natural Products in Cancer Organoid-Immune Co-Cultures: Bridging the Research Gap for Precision Immunotherapy.International journal of molecular sciences · 2025Review
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7 authors.
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Abstract
Intestinal organoids are indispensable tools for exploring intestinal disorders. Deep learning methodologies are often employed in morphological analysis to evaluate the condition of these organoids. Nonetheless, prevailing analytical techniques face obstacles such as many organisational overlaps and tiny targets lead to a high incidence of errors and limited applicability. This paper presents Deliod, a streamlined intestinal organoid detection model founded on YOLOv8 and designed to automate the identification of organoid morphology. Deliod performed excellently compared to leading detection models when applied to an intestinal organoid dataset, attaining an mAP50 of 87.5%. Ablation experiments verified the module's efficacy in improving detection performance. Furthermore, Deliod features a modest parameter count of 5.41 M and a computational load of 16.6 GFLOPs, facilitating the broader application of the detection model in the realm of intestinal organoid image recognition. This streamlined model not only enables efficient and accurate recognition of organoid morphology but also minimizes hardware deployment requirements, broadening its range of potential applications.
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