Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification
<|reference_start|>Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification: 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).<|reference_end|>
@article{li2024domain-invariant,
    title={Domain-invariant Representation Learning via Segment Anything Model for
  Blood Cell Classification},
    author={Yongcheng Li, Lingcong Cai, Ying Lu, Cheng Lin, Yupeng Zhang, Jingyan
  Jiang, Genan Dai, Bowen Zhang, Jingzhou Cao, Xiangzhong Zhang, and Xiaomao
  Fan},
    journal={arXiv preprint arXiv:2408.07467},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07467},
    primaryClass={cs.CV}
}