Microscopic evaluation of white blood cell morphology is central to leukemia diagnosis, yet current deep learning models often act as black boxes, limiting clinical trust and adoption. We introduce HemBLIP, a vision language model designed to generate interpretable, morphology aware descriptions of peripheral blood cells. Using a newly constructed dataset of 14k healthy and leukemic cells paired with expert-derived attribute captions, we adapt a general-purpose VLM via both full fine-tuning and LoRA based parameter efficient training, and benchmark against the biomedical foundation model MedGEMMA. HemBLIP achieves higher caption quality and morphological accuracy, while LoRA adaptation provides further gains with significantly reduced computational cost. These results highlight the promise of vision language models for transparent and scalable hematological diagnostics.

翻译：白细胞形态的显微镜评估是白血病诊断的核心，然而当前的深度学习模型通常作为黑箱运行，限制了临床信任和采用。我们提出了HemBLIP，这是一种视觉语言模型，旨在生成外周血细胞的可解释、具有形态学意识的描述。利用一个新构建的包含14k个健康与白血病细胞及其专家标注属性描述的数据集，我们通过全量微调和基于LoRA的参数高效训练方法，对一个通用视觉语言模型进行适配，并以生物医学基础模型MedGEMMA为基准进行比较。HemBLIP在描述质量和形态学准确性方面表现更优，而LoRA适配在显著降低计算成本的同时带来了进一步的性能提升。这些结果凸显了视觉语言模型在实现透明且可扩展的血液学诊断方面的潜力。