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Blood Cells

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Transferable automatic hematological cell classification: Overcoming data limitations with self-supervised learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Classification of peripheral blood and bone marrow cells is critical in the diagnosis and monitoring of hematological disorders. The development of robust and reliable automatic classification systems is hampered by data sca...

TW-YOLO: An Innovative Blood Cell Detection Model Based on Multi-Scale Feature Fusion.

Sensors (Basel, Switzerland)
As deep learning technology has progressed, automated medical image analysis is becoming ever more crucial in clinical diagnosis. However, due to the diversity and complexity of blood cell images, traditional models still exhibit deficiencies in bloo...

Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology.

BMC medical imaging
The research focuses on the segmentation and classification of leukocytes, a crucial task in medical image analysis for diagnosing various diseases. The leukocyte dataset comprises four classes of images such as monocytes, lymphocytes, eosinophils, a...

Evaluation of deep learning training strategies for the classification of bone marrow cell images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The classification of bone marrow (BM) cells by light microscopy is an important cornerstone of hematological diagnosis, performed thousands of times a day by highly trained specialists in laboratories worldwide. As the manu...

iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays.

Nature communications
While microscopy-based cellular assays, including microfluidics, have significantly advanced over the last several decades, there has not been concurrent development of widely-accessible techniques to analyze time-dependent microscopy data incorporat...

Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Combining knowledge of clinical pathologists and deep learning models is a growing trend in morphological analysis of cells circulating in blood to add objectivity, accuracy, and speed in diagnosing hematological and non-he...

Artificial Intelligence-based online platform assists blood cell morphology learning: A mixed-methods sequential explanatory designed research.

Medical teacher
BACKGROUND: The study aimed to evaluate the effectiveness of learning blood cell morphology by learning on our Artificial intelligence (AI)-based online platform.

Automatic identifying and counting blood cells in smear images by using single shot detector and Taguchi method.

BMC bioinformatics
BACKGROUND: Researchers have tried to identify and count different blood cells in microscopic smear images by using deep learning methods of artificial intelligence to solve the highly time-consuming problem.

Data augmentation with improved regularisation and sampling for imbalanced blood cell image classification.

Scientific reports
Due to progression in cell-cycle or duration of storage, classification of morphological changes in human blood cells is important for correct and effective clinical decisions. Automated classification systems help avoid subjective outcomes and are m...

LeuFeatx: Deep learning-based feature extractor for the diagnosis of acute leukemia from microscopic images of peripheral blood smear.

Computers in biology and medicine
The abnormal growth of leukocytes causes hematologic malignancies such as leukemia. The clinical assessment methods for the diagnosis of the disease are labor-intensive and time-consuming. Image-based automated diagnostic systems can be of great help...