Unsupervised Cross-domain Pulmonary Nodule Detection without Source Data
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866916398101430272 |
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| author | Xu, Rui Luo, Yong Du, Bo |
| author_facet | Xu, Rui Luo, Yong Du, Bo |
| contents | Cross-domain pulmonary nodule detection suffers from performance degradation due to a large shift of data distributions between the source and target domain. Besides, considering the high cost of medical data annotation, it is often assumed that the target images are unlabeled. Existing approaches have made much progress for this unsupervised domain adaptation setting. However, this setting is still rarely plausible in medical applications since the source medical data are often not accessible due to privacy concerns. This motivates us to propose a Source-free Unsupervised cross-domain method for Pulmonary nodule detection (SUP), named Instance-level Contrastive Instruction fine-tuning framework (ICI). It first adapts the source model to the target domain by utilizing instance-level contrastive learning. Then the adapted model is trained in a teacher-student interaction manner, and a weighted entropy loss is incorporated to further improve the accuracy. We establish a benchmark by adapting a pre-trained source model to three popular datasets for pulmonary nodule detection. To the best of our knowledge, this represents the first exploration of source-free unsupervised domain adaptation in medical image object detection. Our extensive evaluations reveal that SUP-ICI substantially surpasses existing state-of-the-art approaches, achieving FROC score improvements ranging from 8.98% to 16.05%. This breakthrough not only sets a new precedent for domain adaptation techniques in medical imaging but also significantly advances the field toward overcoming challenges posed by data privacy and availability. Code: https://github.com/Ruixxxx/SFUDA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_01085 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Unsupervised Cross-domain Pulmonary Nodule Detection without Source Data Xu, Rui Luo, Yong Du, Bo Computer Vision and Pattern Recognition Cross-domain pulmonary nodule detection suffers from performance degradation due to a large shift of data distributions between the source and target domain. Besides, considering the high cost of medical data annotation, it is often assumed that the target images are unlabeled. Existing approaches have made much progress for this unsupervised domain adaptation setting. However, this setting is still rarely plausible in medical applications since the source medical data are often not accessible due to privacy concerns. This motivates us to propose a Source-free Unsupervised cross-domain method for Pulmonary nodule detection (SUP), named Instance-level Contrastive Instruction fine-tuning framework (ICI). It first adapts the source model to the target domain by utilizing instance-level contrastive learning. Then the adapted model is trained in a teacher-student interaction manner, and a weighted entropy loss is incorporated to further improve the accuracy. We establish a benchmark by adapting a pre-trained source model to three popular datasets for pulmonary nodule detection. To the best of our knowledge, this represents the first exploration of source-free unsupervised domain adaptation in medical image object detection. Our extensive evaluations reveal that SUP-ICI substantially surpasses existing state-of-the-art approaches, achieving FROC score improvements ranging from 8.98% to 16.05%. This breakthrough not only sets a new precedent for domain adaptation techniques in medical imaging but also significantly advances the field toward overcoming challenges posed by data privacy and availability. Code: https://github.com/Ruixxxx/SFUDA. |
| title | Unsupervised Cross-domain Pulmonary Nodule Detection without Source Data |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2304.01085 |