Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification

• URL: http://arxiv.org/abs/2408.07467v1
• Date: Wed, 14 Aug 2024 11:24:13 GMT
• Title: Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification
• Authors: Yongcheng Li, Lingcong Cai, Ying Lu, Cheng Lin, Yupeng Zhang, Jingyan Jiang, Genan Dai, Bowen Zhang, Jingzhou Cao, Xiangzhong Zhang, Xiaomao Fan,
• Abstract summary: We propose a novel framework of domain-invariant representation learning (DoRL) for blood cell classification. The advantage of DoRL is that it can extract domain-invariant representations from various blood cell datasets in an unsupervised manner. Our proposed DoRL achieves a new state-of-the-art cross-domain performance, surpassing existing methods by a significant margin.
• Score: 10.237028514911284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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