Multi-instance Learning as Downstream Task of Self-Supervised Learning-based Pre-trained Model
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912398963638272 |
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| author | Matsuishi, Koki Okita, Tsuyoshi |
| author_facet | Matsuishi, Koki Okita, Tsuyoshi |
| contents | In deep multi-instance learning, the number of applicable instances depends on the data set. In histopathology images, deep learning multi-instance learners usually assume there are hundreds to thousands instances in a bag. However, when the number of instances in a bag increases to 256 in brain hematoma CT, learning becomes extremely difficult. In this paper, we address this drawback. To overcome this problem, we propose using a pre-trained model with self-supervised learning for the multi-instance learner as a downstream task. With this method, even when the original target task suffers from the spurious correlation problem, we show improvements of 5% to 13% in accuracy and 40% to 55% in the F1 measure for the hypodensity marker classification of brain hematoma CT. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_21564 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-instance Learning as Downstream Task of Self-Supervised Learning-based Pre-trained Model Matsuishi, Koki Okita, Tsuyoshi Computer Vision and Pattern Recognition Machine Learning In deep multi-instance learning, the number of applicable instances depends on the data set. In histopathology images, deep learning multi-instance learners usually assume there are hundreds to thousands instances in a bag. However, when the number of instances in a bag increases to 256 in brain hematoma CT, learning becomes extremely difficult. In this paper, we address this drawback. To overcome this problem, we propose using a pre-trained model with self-supervised learning for the multi-instance learner as a downstream task. With this method, even when the original target task suffers from the spurious correlation problem, we show improvements of 5% to 13% in accuracy and 40% to 55% in the F1 measure for the hypodensity marker classification of brain hematoma CT. |
| title | Multi-instance Learning as Downstream Task of Self-Supervised Learning-based Pre-trained Model |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2505.21564 |