Enhancing poorly differentiated lung cancer classification with rotary position embedding and sparse attention in multiple instance learning
In clinical practice, diagnosing poorly differentiated non-small cell lung cancer (NSCLC) typically requires immunohistochemistry (IHC) to accurately distinguish between cancer subtypes. The high cost and time-consuming nature of this process significantly limit its clinical applicability. Furthermo...
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| Veröffentlicht in: | Biomedical signal processing and control Jg. 113; S. 108699 |
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01.03.2026
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| ISSN: | 1746-8094 |
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| Abstract | In clinical practice, diagnosing poorly differentiated non-small cell lung cancer (NSCLC) typically requires immunohistochemistry (IHC) to accurately distinguish between cancer subtypes. The high cost and time-consuming nature of this process significantly limit its clinical applicability. Furthermore, the limited availability of tissue samples from advanced-stage patients poses a significant challenge for subsequent diagnosis. Consequently, leveraging novel AI technologies is critical to overcoming this challenge. Hence, we propose an innovative Multi-Instance Learning (MIL) algorithm, RoSA-MIL. This algorithm is based on a novel MIL framework that integrates relative position encoding and sparse attention mechanisms. Relative position encoding enhances the model’s comprehension of spatial relationships between samples, thereby improving its ability to capture local context. The sparse attention mechanism reduces computational overhead by eliminating redundant calculations, thereby enhancing both efficiency and scalability. Experiments conducted on diverse datasets from three independent centers demonstrate that the RoSA-MIL model outperforms existing methods, exhibiting significant improvements in diagnostic accuracy, robustness, and efficiency when processing large-scale data. |
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| AbstractList | In clinical practice, diagnosing poorly differentiated non-small cell lung cancer (NSCLC) typically requires immunohistochemistry (IHC) to accurately distinguish between cancer subtypes. The high cost and time-consuming nature of this process significantly limit its clinical applicability. Furthermore, the limited availability of tissue samples from advanced-stage patients poses a significant challenge for subsequent diagnosis. Consequently, leveraging novel AI technologies is critical to overcoming this challenge. Hence, we propose an innovative Multi-Instance Learning (MIL) algorithm, RoSA-MIL. This algorithm is based on a novel MIL framework that integrates relative position encoding and sparse attention mechanisms. Relative position encoding enhances the model’s comprehension of spatial relationships between samples, thereby improving its ability to capture local context. The sparse attention mechanism reduces computational overhead by eliminating redundant calculations, thereby enhancing both efficiency and scalability. Experiments conducted on diverse datasets from three independent centers demonstrate that the RoSA-MIL model outperforms existing methods, exhibiting significant improvements in diagnostic accuracy, robustness, and efficiency when processing large-scale data. |
| ArticleNumber | 108699 |
| Author | Shao, Zihan Wang, Yang Guan, Tian Yang, Jiao Li, Xiaomei Guo, Xiaojing Chang, Zhengsong Chu, Hongbo Deng, Pengyun Wang, Yan |
| Author_xml | – sequence: 1 givenname: Hongbo surname: Chu fullname: Chu, Hongbo organization: Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China – sequence: 2 givenname: Zhengsong surname: Chang fullname: Chang, Zhengsong organization: Department of Pathology, Harbin Medical University Cancer Hospital, 150 Haping road, Nangang District, Harbin 150081, China – sequence: 3 givenname: Zihan surname: Shao fullname: Shao, Zihan organization: School of Life Sciences, Anhui University, China – sequence: 4 givenname: Pengyun surname: Deng fullname: Deng, Pengyun organization: Department of Pathology, Harbin Medical University Cancer Hospital, 150 Haping road, Nangang District, Harbin 150081, China – sequence: 5 givenname: Yan surname: Wang fullname: Wang, Yan organization: Department of Pathology, Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518001, China – sequence: 6 givenname: Xiaojing surname: Guo fullname: Guo, Xiaojing organization: Department of Pathology, Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518001, China – sequence: 7 givenname: Yang surname: Wang fullname: Wang, Yang organization: Department of Pathology, Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518001, China – sequence: 8 givenname: Jiao surname: Yang fullname: Yang, Jiao organization: Department of Pathology, Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518001, China – sequence: 9 givenname: Tian surname: Guan fullname: Guan, Tian email: guantian@sz.tsinghua.edu.cn organization: Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China – sequence: 10 givenname: Xiaomei surname: Li fullname: Li, Xiaomei email: lijingfmmu@fmmu.edu.cn organization: Department of Pathology, Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518001, China |
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| Cites_doi | 10.1109/CVPR52688.2022.01825 10.1038/s41746-024-01003-0 10.4103/2153-3539.119005 10.1016/j.neucom.2023.127063 10.1109/ICCVW54120.2021.00081 10.1038/s41591-024-02857-3 10.1145/3664647.3681425 10.1109/ICCV48922.2021.00986 10.1109/CVPR52688.2022.01824 10.1109/CVPR52733.2024.01076 10.1109/CVPR46437.2021.01409 10.1038/s41591-024-02856-4 10.1109/CVPR52729.2023.00720 10.1109/CVPR52729.2023.01503 10.1038/s41551-020-00682-w 10.1609/aaai.v35i16.17664 10.6004/jnccn.2022.0025 |
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| Keywords | Deep learning Transformer Histopathological classification Poorly differentiated lung cancer Multiple instance learning |
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