Towards Cross-Domain Single Blood Cell Image Classification via Large-Scale LoRA-based Segment Anything Model
<|reference_start|>Towards Cross-Domain Single Blood Cell Image Classification via Large-Scale LoRA-based Segment Anything Model: Accurate classification of blood cells plays a vital role in hematological analysis as it aids physicians in diagnosing various medical conditions. In this study, we present a novel approach for classifying blood cell images known as BC-SAM. BC-SAM leverages the large-scale foundation model of Segment Anything Model (SAM) and incorporates a fine-tuning technique using LoRA, allowing it to extract general image embeddings from blood cell images. To enhance the applicability of BC-SAM across different blood cell image datasets, we introduce an unsupervised cross-domain autoencoder that focuses on learning intrinsic features while suppressing artifacts in the images. To assess the performance of BC-SAM, we employ four widely used machine learning classifiers (Random Forest, Support Vector Machine, Artificial Neural Network, and XGBoost) to construct blood cell classification models and compare them against existing state-of-the-art methods. Experimental results conducted on two publicly available blood cell datasets (Matek-19 and Acevedo-20) demonstrate that our proposed BC-SAM achieves a new state-of-the-art result, surpassing the baseline methods with a significant improvement. The source code of this paper is available at https://github.com/AnoK3111/BC-SAM.<|reference_end|>
@article{li2024towards,
    title={Towards Cross-Domain Single Blood Cell Image Classification via
  Large-Scale LoRA-based Segment Anything Model},
    author={Yongcheng Li, Lingcong Cai, Ying Lu, Yupeng Zhang, Jingyan Jiang,
  Genan Dai, Bowen Zhang, Jingzhou Cao, Xiangzhong Zhang, and Xiaomao Fan},
    journal={arXiv preprint arXiv:2408.06716},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06716},
    primaryClass={cs.CV}
}