Domain-invariant representation learning via SAM for blood cell classification

Accurate classification of blood cells is of vital significance in the diagnosis of hematological disorders, facilitating timely treatments for patients. However, in real-world scenarios, domain shifts caused by the variability in laboratory procedures and settings often result in rapid deterioration in model generalization performance. To address this issue, we propose a novel domain-invariant representation learning via the Segment Anything Model (SAM) for blood cell classification, referred to as DoRL. The DoRL comprises two main components: a LoRA-based SAM (LoRA-SAM) and a cross-domain autoencoder (CAE). The key advantage of DoRL is the ability to extract domain-invariant representations from various blood cell datasets in an unsupervised manner. Specifically, we first leverage the large-scale foundation model SAM, fine-tuned with LoRA, to generate robust and transferable visual representations of blood cells. Furthermore, we introduce the CAE to learn domain-invariant representations from the image embeddings across different-domain datasets. The CAE mitigates the impact of image artifacts and other domain-specific variations, ensuring the learned representations more generalizable. 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-world dataset demonstrate that our proposed DoRL achieves a new state-of-the-art cross-domain performance, surpassing existing methods by a significant margin. The DoRL, with its novel integration of LoRA-SAM and cross-domain autoencoding, provides a robust and effective solution for enhancing the generalization capabilities of blood cell classification models, potentially improving patient care and outcomes. The source code can be available at https://github.com/AnoK3111/DoRL.