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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.07467 |
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| _version_ | 1866910565420498944 |
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| author | Li, Yongcheng Cai, Lingcong Lu, Ying Lin, Cheng Zhang, Yupeng Jiang, Jingyan Dai, Genan Zhang, Bowen Cao, Jingzhou Zhang, Xiangzhong Fan, Xiaomao |
| author_facet | Li, Yongcheng Cai, Lingcong Lu, Ying Lin, Cheng Zhang, Yupeng Jiang, Jingyan Dai, Genan Zhang, Bowen Cao, Jingzhou Zhang, Xiangzhong Fan, Xiaomao |
| contents | Accurate classification of blood cells is of vital significance in the diagnosis of hematological disorders. However, in real-world scenarios, domain shifts caused by the variability in laboratory procedures and settings, result in a rapid deterioration of the model's generalization performance. To address this issue, we propose a novel framework of domain-invariant representation learning (DoRL) via segment anything model (SAM) for blood cell classification. The DoRL comprises two main components: a LoRA-based SAM (LoRA-SAM) and a cross-domain autoencoder (CAE). The advantage of DoRL is that it can extract domain-invariant representations from various blood cell datasets in an unsupervised manner. Specifically, we first leverage the large-scale foundation model of SAM, fine-tuned with LoRA, to learn general image embeddings and segment blood cells. Additionally, we introduce CAE to learn domain-invariant representations across different-domain datasets while mitigating images' artifacts. To validate the effectiveness of domain-invariant representations, we employ five widely used machine learning classifiers to construct blood cell classification models. Experimental results on two public blood cell datasets and a private real dataset demonstrate that our proposed DoRL achieves a new state-of-the-art cross-domain performance, surpassing existing methods by a significant margin. The source code can be available at the URL (https://github.com/AnoK3111/DoRL). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07467 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification Li, Yongcheng Cai, Lingcong Lu, Ying Lin, Cheng Zhang, Yupeng Jiang, Jingyan Dai, Genan Zhang, Bowen Cao, Jingzhou Zhang, Xiangzhong Fan, Xiaomao Computer Vision and Pattern Recognition Accurate classification of blood cells is of vital significance in the diagnosis of hematological disorders. However, in real-world scenarios, domain shifts caused by the variability in laboratory procedures and settings, result in a rapid deterioration of the model's generalization performance. To address this issue, we propose a novel framework of domain-invariant representation learning (DoRL) via segment anything model (SAM) for blood cell classification. The DoRL comprises two main components: a LoRA-based SAM (LoRA-SAM) and a cross-domain autoencoder (CAE). The advantage of DoRL is that it can extract domain-invariant representations from various blood cell datasets in an unsupervised manner. Specifically, we first leverage the large-scale foundation model of SAM, fine-tuned with LoRA, to learn general image embeddings and segment blood cells. Additionally, we introduce CAE to learn domain-invariant representations across different-domain datasets while mitigating images' artifacts. To validate the effectiveness of domain-invariant representations, we employ five widely used machine learning classifiers to construct blood cell classification models. Experimental results on two public blood cell datasets and a private real dataset demonstrate that our proposed DoRL achieves a new state-of-the-art cross-domain performance, surpassing existing methods by a significant margin. The source code can be available at the URL (https://github.com/AnoK3111/DoRL). |
| title | Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.07467 |