Attention-based deep learning for accurate cell image analysis
Abstract High-content analysis (HCA) holds enormous potential for drug discovery and research, but widely used methods can be cumbersome and yield inaccurate results. Noisy and redundant signals in cell images impede accurate deep learning-based image analysis. To address these issues, we introduce...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-85608-9 |
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| _version_ | 1841544798943576064 |
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| author | Xiangrui Gao Fan Zhang Xueyu Guo Mengcheng Yao Xiaoxiao Wang Dong Chen Genwei Zhang Xiaodong Wang Lipeng Lai |
| author_facet | Xiangrui Gao Fan Zhang Xueyu Guo Mengcheng Yao Xiaoxiao Wang Dong Chen Genwei Zhang Xiaodong Wang Lipeng Lai |
| author_sort | Xiangrui Gao |
| collection | DOAJ |
| description | Abstract High-content analysis (HCA) holds enormous potential for drug discovery and research, but widely used methods can be cumbersome and yield inaccurate results. Noisy and redundant signals in cell images impede accurate deep learning-based image analysis. To address these issues, we introduce X-Profiler, a novel HCA method that combines cellular experiments, image processing, and deep learning modeling. X-Profiler combines the convolutional neural network and Transformer to encode high-content images, effectively filtering out noisy signals and precisely characterizing cell phenotypes. In comparative tests on drug-induced cardiotoxicity, mitochondrial toxicity classification, and compound classification, X-Profiler outperformed both DeepProfiler and CellProfiler, as two highly recognized and representative methods in this field. Our results demonstrate the utility and versatility of X-Profiler, and we anticipate its wide application in HCA for advancing drug development and disease research. |
| format | Article |
| id | doaj-art-162e2cacb3d34b8e988ab50a2004f9ad |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-162e2cacb3d34b8e988ab50a2004f9ad2025-01-12T12:15:08ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-025-85608-9Attention-based deep learning for accurate cell image analysisXiangrui Gao0Fan Zhang1Xueyu Guo2Mengcheng Yao3Xiaoxiao Wang4Dong Chen5Genwei Zhang6Xiaodong Wang7Lipeng Lai8XtalPi Innovation CenterXtalPi Innovation CenterXtalPi Innovation CenterXtalPi Innovation CenterXtalPi Innovation CenterXtalPi Innovation CenterXtalPi Innovation CenterXtalPi Innovation CenterXtalPi Innovation CenterAbstract High-content analysis (HCA) holds enormous potential for drug discovery and research, but widely used methods can be cumbersome and yield inaccurate results. Noisy and redundant signals in cell images impede accurate deep learning-based image analysis. To address these issues, we introduce X-Profiler, a novel HCA method that combines cellular experiments, image processing, and deep learning modeling. X-Profiler combines the convolutional neural network and Transformer to encode high-content images, effectively filtering out noisy signals and precisely characterizing cell phenotypes. In comparative tests on drug-induced cardiotoxicity, mitochondrial toxicity classification, and compound classification, X-Profiler outperformed both DeepProfiler and CellProfiler, as two highly recognized and representative methods in this field. Our results demonstrate the utility and versatility of X-Profiler, and we anticipate its wide application in HCA for advancing drug development and disease research.https://doi.org/10.1038/s41598-025-85608-9 |
| spellingShingle | Xiangrui Gao Fan Zhang Xueyu Guo Mengcheng Yao Xiaoxiao Wang Dong Chen Genwei Zhang Xiaodong Wang Lipeng Lai Attention-based deep learning for accurate cell image analysis Scientific Reports |
| title | Attention-based deep learning for accurate cell image analysis |
| title_full | Attention-based deep learning for accurate cell image analysis |
| title_fullStr | Attention-based deep learning for accurate cell image analysis |
| title_full_unstemmed | Attention-based deep learning for accurate cell image analysis |
| title_short | Attention-based deep learning for accurate cell image analysis |
| title_sort | attention based deep learning for accurate cell image analysis |
| url | https://doi.org/10.1038/s41598-025-85608-9 |
| work_keys_str_mv | AT xiangruigao attentionbaseddeeplearningforaccuratecellimageanalysis AT fanzhang attentionbaseddeeplearningforaccuratecellimageanalysis AT xueyuguo attentionbaseddeeplearningforaccuratecellimageanalysis AT mengchengyao attentionbaseddeeplearningforaccuratecellimageanalysis AT xiaoxiaowang attentionbaseddeeplearningforaccuratecellimageanalysis AT dongchen attentionbaseddeeplearningforaccuratecellimageanalysis AT genweizhang attentionbaseddeeplearningforaccuratecellimageanalysis AT xiaodongwang attentionbaseddeeplearningforaccuratecellimageanalysis AT lipenglai attentionbaseddeeplearningforaccuratecellimageanalysis |