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Blood Cells

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ReRNet: A Deep Learning Network for Classifying Blood Cells.

Technology in cancer research & treatment
AIMS: Blood cell classification helps detect various diseases. However, the current classification model of blood cells cannot always get great results. A network that automatically classifies blood cells can provide doctors with data as one of the c...

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 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...

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...

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...

[Chinese expert consensus on the technical and clinical practice specifications of artificial intelligence assisted morphology examination of blood cells (2024)].

Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi
Blood cell morphological examination is a crucial method for the diagnosis of blood diseases, but traditional manual microscopy is characterized by low efficiency and susceptibility to subjective biases. The application of artificial intelligence (AI...

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...

[Opportunities and expectations brought by artificial intelligence assisted peripheral blood cell morphology examination].

Zhonghua yi xue za zhi
The morphological examination of blood cells under manual microscopes is a classic method, but the obvious shortcomings limit the extensive development of peripheral blood cell morphological examination. By using the manual microscope method, it is d...

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...

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...