AttriGen: Automated Multi-Attribute Annotation for Blood Cell Datasets
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912618030039040 |
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| author | Houmaidi, Walid Sabiri, Youssef Iguenfer, Fatima Zahra Abouaomar, Amine |
| author_facet | Houmaidi, Walid Sabiri, Youssef Iguenfer, Fatima Zahra Abouaomar, Amine |
| contents | We introduce AttriGen, a novel framework for automated, fine-grained multi-attribute annotation in computer vision, with a particular focus on cell microscopy where multi-attribute classification remains underrepresented compared to traditional cell type categorization. Using two complementary datasets: the Peripheral Blood Cell (PBC) dataset containing eight distinct cell types and the WBC Attribute Dataset (WBCAtt) that contains their corresponding 11 morphological attributes, we propose a dual-model architecture that combines a CNN for cell type classification, as well as a Vision Transformer (ViT) for multi-attribute classification achieving a new benchmark of 94.62\% accuracy. Our experiments demonstrate that AttriGen significantly enhances model interpretability and offers substantial time and cost efficiency relative to conventional full-scale human annotation. Thus, our framework establishes a new paradigm that can be extended to other computer vision classification tasks by effectively automating the expansion of multi-attribute labels. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_26185 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AttriGen: Automated Multi-Attribute Annotation for Blood Cell Datasets Houmaidi, Walid Sabiri, Youssef Iguenfer, Fatima Zahra Abouaomar, Amine Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 60G35, 62M10, 62P35, 65C20, 68T45, 68U10, 92C35, 92C40, 92C42, 93E10 I.4; I.4.8; I.4.9; I.4.10; I.2; I.2.6; I.2.10; J.3; C.2.4; C.3; H.2.8; H.3.4; H.3.5; I.2.4; I.5; I.5.1; I.5.4; K.6.1 We introduce AttriGen, a novel framework for automated, fine-grained multi-attribute annotation in computer vision, with a particular focus on cell microscopy where multi-attribute classification remains underrepresented compared to traditional cell type categorization. Using two complementary datasets: the Peripheral Blood Cell (PBC) dataset containing eight distinct cell types and the WBC Attribute Dataset (WBCAtt) that contains their corresponding 11 morphological attributes, we propose a dual-model architecture that combines a CNN for cell type classification, as well as a Vision Transformer (ViT) for multi-attribute classification achieving a new benchmark of 94.62\% accuracy. Our experiments demonstrate that AttriGen significantly enhances model interpretability and offers substantial time and cost efficiency relative to conventional full-scale human annotation. Thus, our framework establishes a new paradigm that can be extended to other computer vision classification tasks by effectively automating the expansion of multi-attribute labels. |
| title | AttriGen: Automated Multi-Attribute Annotation for Blood Cell Datasets |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 60G35, 62M10, 62P35, 65C20, 68T45, 68U10, 92C35, 92C40, 92C42, 93E10 I.4; I.4.8; I.4.9; I.4.10; I.2; I.2.6; I.2.10; J.3; C.2.4; C.3; H.2.8; H.3.4; H.3.5; I.2.4; I.5; I.5.1; I.5.4; K.6.1 |
| url | https://arxiv.org/abs/2509.26185 |