This paper proposes a novel automatic classification framework for the recognition of five types of white blood cells. Segmenting complete white blood cells from blood smears images and extracting advantageous features from them remain challenging tasks in the classification of white blood cells. Therefore, we present an adaptive threshold segmentation method to deal with blood smears images with non-uniform color and uneven illumination, which is designed based on color space information and threshold segmentation. Subsequently, after successfully separating the white blood cell from the blood smear image, a large number of nonlinear features including geometrical, color and texture features are extracted. Nevertheless, redundant features can affect the classification speed and efficiency, and in view of that, a feature selection algorithm based on classification and regression trees (CART) is designed. Through in-depth analysis of the nonlinear relationship between features, the irrelevant and redundant features are successfully removed from the initial nonlinear features. Afterwards, the selected prominent features are fed into particle swarm optimization support vector machine (PSO-SVM) classifier to recognize the types of the white blood cells. Finally, to evaluate the performance of the proposed white blood cell classification methodology, we build a white blood cell data set containing 500 blood smear images for experiments. By comparing with the ground truth obtained manually, the proposed segmentation method achieves an average of 95.98% and 97.57% dice similarity for segmented nucleus and cell regions respectively. Furthermore, the proposed methodology achieves 99.76% classification accuracy, which well demonstrates its effectiveness.
Neural Cellular Automata for Weakly Supervised Segmentation of White Blood Cells
Institute of AI for Health, Helmholtz Zentrum München – German Research Center for Environmental Health（人工智能与健康研究所，慕尼黑德国环境健康研究中心）；TUM School of Computation, Information and Technology, Technical University Munich（技术大学慕尼黑计算、信息与技术学院）；Institute of Chemical Epigenetics, Faculty of Chemistry and Pharmacy, Ludwig Maximilian University（化学表观遗传学研究所，路德维希-马克西米利安大学）；Computer Aided Medical Procedures, Technical University of Munich（医学辅助程序研究所，技术大学慕尼黑）
Robust White Blood Cell Classification with Stain-Normalized Decoupled Learning and Ensembling
University of Technology, Ho Chi Minh City, Vietnam（越南胡志明市技术大学）；University of Science, Ho Chi Minh City, Vietnam（越南胡志明市科学大学）；Vietnam National University, Ho Chi Minh City, Vietnam（越南国家大学）；Carnegie Mellon University, Pittsburgh, PA, USA（卡内基梅隆大学）；Northwestern University, Chicago, IL, USA（西北大学）
A Hierarchical Ensemble Inference Pipeline for Robust White Blood Cell Classification Under Domain Shifts
Department of Computer Science and Engineering（计算机科学与工程系）；The Hong Kong University of Science and Technology（香港理工大学）；Department of Chemical and Biological Engineering（化学与生物工程系）；Division of Life Science（生命科学系）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳-香港协同创新研究院）；State Key Laboratory of Nervous System Disorders（神经系统疾病国家重点实验室）
Synergizing Deep Learning and Biological Heuristics for Extreme Long-Tail White Blood Cell Classification
VinUni-Illinois Smart Health Center, VinUniversity（VinUniversity-VinUni-Illinois智能健康中心）；The Computer Vision and Medical AI Lab, VinUniversity（VinUniversity计算机视觉与医学人工智能实验室）
WBCAtt+: Fine-Grained Pixel-Level Morphological Annotations for White Blood Cell Images
Rapid-Rich Object Search (ROSE) Lab, School of Electrical and Electronic Engineering, Nanyang Technological University（快速丰富目标搜索（ROSE）实验室，电气与电子工程学院，南洋理工大学）；Shanghai University of Finance and Economics（上海财经大学）
Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification
College of Big Data and Internet, Shenzhen Technology University（深圳技术大学大数据与互联网学院）；The Third Affiliated Hospital of Sun Yat-sen University（中山大学附属第三医院）；School of Computer Science, South China Normal University（华南师范大学计算机学院）
A Continual Learning Approach for Cross-Domain White Blood Cell Classification
Helmholtz Munich – German Research Center for Environmental Health（亥姆霍兹慕尼黑-德国环境健康研究中心）；Technical University of Munich（慕尼黑工业大学）；Ludwig-Maximilian University Munich（路德维希-马克西米利安慕尼黑大学）；University Hospital, Ludwig-Maximilians University Munich（慕尼黑大学附属医院）；Roche Innovation Center Munich (RICM)（罗氏慕尼黑创新中心）；Johns Hopkins University（约翰斯·霍普金斯大学）
Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images
Institute of AI for Health, Helmholtz Zentrum München – German Research Center for Environmental Health（慕尼黑亥姆霍兹中心人工智能健康研究所（德国环境健康研究中心））；Faculty of Mathematics, Technical University Munich（慕尼黑工业大学数学学院）；Computer Aided Medical Procedures, Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序研究所）；Computer Aided Medical Procedures, Johns Hopkins University（约翰霍普金斯大学计算机辅助医疗程序研究所）；Helmholtz AI, Helmholtz Zentrum München - German Research Center for Environmental Health（亥姆霍兹人工智能中心（慕尼黑亥姆霍兹中心，德国环境健康研究中心））
TE-YOLOF: Tiny and efficient YOLOF for blood cell detection
Southwest Minzu University（西南民族大学）；West China Biomedical Big Data Center（华西生物医学大数据中心）
显微图像中的血细胞检测是医学图像处理研究的一个重要分支。由于基于手动检查血细胞的疾病检测耗时且充满错误，因此使用具有深度卷积神经网络的对象检测器来检测血细胞可以被视为可行的解决方案。在这项工作中，基于YOLOF的对象检测器已经被提出来检测血细胞对象，如红细胞，白细胞和血小板。这个目标检测器被称为TE-YOLOF，Tiny and Efficient YOLOF，它是一个使用膨胀编码器从单级特征图中提取信息的一级检测器。为了提高效率和灵活性，EfficientNet卷积神经网络被用作所提出的对象检测器的骨干。此外，还采用了相关可分离卷积来提高网络的性能，并使网络参数最小化。此外，还采用了Mish激活函数来提高识别精度。在BCCD数据集上的大量实验证明了该模型的有效性，该模型比其他现有的血细胞检测研究更有效。
Classification of White Blood Cells Using Machine and Deep Learning Models: A Systematic Review
University of Lahore（拉合尔大学）；University of Limerick（利默里克大学）；Dublin Institute of Technology（都柏林理工学院）；National University of Sciences and Technology（国家科技大学）
Semi-Supervised Semantic Segmentation using Redesigned Self-Training for White Blood Cells
Ho Chi Minh University of Technology（胡志明市技术大学）；Taipei Medical University（台北医学大学）；Ho Chi Minh City Medicine and Pharmacy University（胡志明市医药大学）；Vietnamese-German University（越南德国大学）
Multimodal Analysis of White Blood Cell Differentiation in Acute Myeloid Leukemia Patients using a β-Variational Autoencoder
Institute of AI for Health, Helmholtz Zentrum München – German Research Center for Environmental Health（慕尼黑亥姆霍兹中心人工智能健康研究所（德国环境健康研究中心））；University of Trento（特伦托大学）；Technical University of Munich（慕尼黑工业大学）；Ludwig-Maximilians University Munich（慕尼黑大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Attention based Multiple Instance Learning for Classification of Blood Cell Disorders
Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health（亥姆霍兹慕尼黑中心-德国环境健康研究中心计算生物学研究所）；Computer Aided Medical Procedures, Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序研究所）；Helmholtz AI, Helmholtz Center Munich（慕尼黑亥姆霍兹中心亥姆霍兹人工智能）；Red Blood Cell Research Group, Institute of Veterinary Physiology, Vetsuisse Faculty and the Zurich Center for Integrative Human Physiology, University of Zurich（苏黎世大学兽医学院兽医生理学研究所红细胞研究组及苏黎世综合人类生理学中心）；Computer Aided Medical Procedures, Johns Hopkins University（约翰斯·霍普金斯大学计算机辅助医疗程序研究所）；Computer Vision Lab (CVL), ETH Zurich（苏黎世联邦理工学院计算机视觉实验室）
Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features
Center for Public Health Genomics（公共卫生基因组学中心）；Department of Biomedical Engineering University of Virginia（弗吉尼亚大学生物医学工程系）；University of Virginia（弗吉尼亚大学）
Convolutional Neural Network and decision support in medical imaging: case study of the recognition of blood cell subtypes
Ecole Supérieure Polytechnique, Université cheikh Anta Diop（谢赫·安塔·迪奥普大学高等理工学院）；Institut national Supérieur de l’Education Populaire et du Sport, Université cheikh Anta Diop（谢赫·安塔·迪奥普大学国家高等民众教育与体育学院）；Faculté de Médecine, de Pharmacie et d’Odonto-Stomatologie, Université cheikh Anta Diop（谢赫·安塔·迪奥普大学医学、药学与牙科学院）
Ensemble of Small Classifiers For Imbalanced White Blood Cell Classification
Department of Computer Science, University of Warwick（沃里克大学计算机科学系）；Warwick Medical School, University of Warwick（沃里克医学院）；Intelligent Imaging Innovations Ltd（智能影像创新有限公司）
Diagnosis Support of Sickle Cell Anemia by Classifying Red Blood Cell Shape in Peripheral Blood Images
Facultad de Ciencias Naturales y Exactas, Universidad de Oriente（自然科学与精确科学系，奥里ente大学）；Balearic Islands Health Research Institute (IdISBa)（巴利阿里群岛健康研究 institutes（IdISBa））；Department of Mathematics and Computer Science, Universitat de les Illes Balears（数学与计算机科学系，巴利阿里群岛大学）；Research Institute of Health Sciences (IUNICS)（健康科学研究院（IUNICS））；Computational biology and bioinformatics (BIOCOM) Research Group（计算生物学和生物信息学（BIOCOM）研究组）
Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification
Institute of AI for Health, Helmholtz Munich – German Research Center for Environmental Health（亥姆霍兹慕尼黑中心——德国环境健康研究中心健康人工智能研究所）；Politecnico Di Torino（都灵理工大学）；Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University Hospital Bonn（波恩大学医院）；Helmholtz AI, Helmholtz Munich – German Research Center for Environmental Health（亥姆霍兹慕尼黑中心——德国环境健康研究中心亥姆霍兹人工智能）
BloodCell-Net: A lightweight convolutional neural network for the classification of all microscopic blood cell images of the human body
Khulna University of Engineering and Technology（库尔纳工程技术大学）；Bangladesh Atomic Energy Regulatory Authority（孟加拉国原子能监管局）；Zarqa University（扎尔卡大学）；Northumbria University（诺森比亚大学）；Bangabandhu Sheikh Mujibur Rahman Science and Technology University（班加班杜谢赫穆吉布尔·拉赫曼科技大学）；Northern Border University（北方边境大学）
Pathologist-Like Explanations Unveiled: an Explainable Deep Learning System for White Blood Cell Classification
Indian Statistical Institute（印度统计研究所）；Drs Tribedi & Roy Diagnostic Laboratory（特里贝迪-罗伊医生诊断实验室）；Zoho Corporation（卓豪公司）；Centre for Digital Healthcare Technologies, MAKAUT（MAKAUT数字医疗技术中心）
Domain-incremental White Blood Cell Classification with Privacy-aware Continual Learning
University of Regensburg（雷根斯堡大学）；University Hospital RWTH Aachen（亚琛工业大学附属医院）；University Hospital Erlangen（埃尔朗根大学附属医院）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学梅维斯研究所）
