In hematology, computational models offer significant potential to improve
diagnostic accuracy, streamline workflows, and reduce the tedious work of
analyzing single cells in peripheral blood or bone marrow smears. However,
clinical adoption of computational models has been hampered by the lack of
generalization due to large batch effects, small dataset sizes, and poor
performance in transfer learning from natural images. To address these
challenges, we introduce DinoBloom, the first foundation model for single cell
images in hematology, utilizing a tailored DINOv2 pipeline. Our model is built
upon an extensive collection of 13 diverse, publicly available datasets of
peripheral blood and bone marrow smears, the most substantial open-source
cohort in hematology so far, comprising over 380,000 white blood cell images.
To assess its generalization capability, we evaluate it on an external dataset
with a challenging domain shift. We show that our model outperforms existing
medical and non-medical vision models in (i) linear probing and k-nearest
neighbor evaluations for cell-type classification on blood and bone marrow
smears and (ii) weakly supervised multiple instance learning for acute myeloid
leukemia subtyping by a large margin. A family of four DinoBloom models (small,
base, large, and giant) can be adapted for a wide range of downstream
applications, be a strong baseline for classification problems, and facilitate
the assessment of batch effects in new datasets. All models are available at
github.com/marrlab/DinoBloom.

血液病学中，计算模型具有显著的潜力，以提高诊断准确性、简化工作流程，并减少分析外周血细胞或骨髓涂片中的单个细胞的乏味工作。为解决大数据量效应不明显、数据集规模小以及从自然图像转移学习性能差的问题，我们介绍 DinoBloom，这是血液病学中首个基于单个细胞图像的基础模型，利用了定制的 DINOv2 流程。我们的模型基于 13 个多样化的公开数据集构建而成，包含了迄今为止最大的血液病学开源队列，包括超过 380,000 个白细胞图像的外周血和骨髓涂片。我们评估其泛化能力时，使用具有挑战性的域转移的外部数据集进行测试。我们展示了我们的模型在血液和骨髓涂片的细胞类型分类方面，在线性探测和 K 近邻评估，以及通过弱监督多实例学习对急性髓系白血病亚型进行分类方面，都优于现有的医学和非医学视觉模型。DinoBloom 系列模型（small、base、large 和 giant）可以适应各种下游应用，并成为分类问题的一个强有力的基线，同时有助于评估新数据集中的大数据批次效应。所有模型均可在 github.com/marrlab/DinoBloom 上获取。

DinoBloom：一种用于血液学中可推广的细胞嵌入的基础模型

DinoBloom: A Foundation Model for Generalizable Cell Embeddings in  Hematology

Object detection and localization are crucial tasks for biomedical image
analysis, particularly in the field of hematology where the detection and
recognition of blood cells are essential for diagnosis and treatment decisions.
While attention-based methods have shown significant progress in object
detection in various domains, their application in medical object detection has
been limited due to the unique challenges posed by medical imaging datasets. To
address this issue, we propose ADA-YOLO, a light-weight yet effective method
for medical object detection that integrates attention-based mechanisms with
the YOLOv8 architecture. Our proposed method leverages the dynamic feature
localisation and parallel regression for computer vision tasks through
\textit{adaptive head} module. Empirical experiments were conducted on the
Blood Cell Count and Detection (BCCD) dataset to evaluate the effectiveness of
ADA-YOLO. The results showed that ADA-YOLO outperforms the YOLOv8 model in mAP
(mean average precision) on the BCCD dataset by using more than 3 times less
space than YOLOv8. This indicates that our proposed method is effective.
Moreover, the light-weight nature of our proposed method makes it suitable for
deployment in resource-constrained environments such as mobile devices or edge
computing systems. which could ultimately lead to improved diagnosis and
treatment outcomes in the field of hematology.

基于注意力机制的医学目标检测方法 ADA-YOLO 在血细胞计数和检测 (BCCD) 数据集上表现出色，相较于 YOLOv8 模型，它在 mAP（平均准确率）上优于后者，并仅使用了三倍以上的空间，适用于资源受限的环境，如移动设备或边缘计算系统。