AIMC Topic: Leukocytes

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A deep learning method for counting white blood cells in bone marrow images.

BMC bioinformatics
BACKGROUND: Differentiating and counting various types of white blood cells (WBC) in bone marrow smears allows the detection of infection, anemia, and leukemia or analysis of a process of treatment. However, manually locating, identifying, and counti...

New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images.

Scientific reports
This article addresses a new method for the classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting featur...

Swarm Learning for decentralized and confidential clinical machine learning.

Nature
Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an i...

Tens of images can suffice to train neural networks for malignant leukocyte detection.

Scientific reports
Convolutional neural networks (CNNs) excel as powerful tools for biomedical image classification. It is commonly assumed that training CNNs requires large amounts of annotated data. This is a bottleneck in many medical applications where annotation r...

Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer's disease.

PloS one
We evaluated the utility of leucocyte epigenomic-biomarkers for Alzheimer's Disease (AD) detection and elucidates its molecular pathogeneses. Genome-wide DNA methylation analysis was performed using the Infinium MethylationEPIC BeadChip array in 24 l...

Deep learning-based enhancement of epigenomics data with AtacWorks.

Nature communications
ATAC-seq is a widely-applied assay used to measure genome-wide chromatin accessibility; however, its ability to detect active regulatory regions can depend on the depth of sequencing coverage and the signal-to-noise ratio. Here we introduce AtacWorks...

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission.

Frontiers in immunology
A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subsequent death. Owing to the heterogeneity, complexity, and unpredictability of the disease progression,...

Improving blood cells classification in peripheral blood smears using enhanced incremental training.

Computers in biology and medicine
Peripheral Blood Smear (PBS) analysis is a vital routine test carried out by medical specialists to assess some health aspects of individuals. The automation of blood analysis has attracted the attention of researchers in recent years, as it will not...

Antibody Supervised Training of a Deep Learning Based Algorithm for Leukocyte Segmentation in Papillary Thyroid Carcinoma.

IEEE journal of biomedical and health informatics
The quantity of leukocytes in papillary thyroid carcinoma (PTC) potentially have prognostic and treatment predictive value. Here, we propose a novel method for training a convolutional neural network (CNN) algorithm for segmenting leukocytes in PTCs....