ArticleToxicological research2023
A comparative study on the implementation of deep learning algorithms for detection of hepatic necrosis in toxicity studies.
Article in Toxicological research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Comparative analysis of chronic progressive nephropathy (CPN) diagnosis in rat kidneys using an artificial intelligence deep learning model.Toxicological research · 2024Article
- Deep Learning-based Modeling for Preclinical Drug Safety Assessment.bioRxiv : the preprint server for biology · 2024Article
- Combination of pathological, biochemical and behavioral evaluations for peripheral neurotoxicity assessment in isoniazid-treated rats.Journal of toxicologic pathology · 2024Article
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9 authors.
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Abstract
Deep learning has recently become one of the most popular methods of image analysis. In non-clinical studies, several tissue slides are generated to investigate the toxicity of a test compound. These are converted into digital image data using a slide scanner, which is then studied by researchers to investigate abnormalities, and the deep learning method has been started to adopt in this study. However, comparative studies evaluating different deep learning algorithms for analyzing abnormal lesions are scarce. In this study, we applied three algorithms, SSD, Mask R-CNN, and DeepLabV3 Supplementary Information: The online version contains supplementary material available at 10.1007/s43188-023-00173-5.
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