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Microscopy

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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...

An End-to-End Platform for Digital Pathology Using Hyperspectral Autofluorescence Microscopy and Deep Learning-Based Virtual Histology.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Conventional histopathology involves expensive and labor-intensive processes that often consume tissue samples, rendering them unavailable for other analyses. We present a novel end-to-end workflow for pathology powered by hyperspectral microscopy an...

Engineering the future of 3D pathology.

The journal of pathology. Clinical research
In recent years, technological advances in tissue preparation, high-throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high-resolution histologic datasets are ob...

Deep learning in mesoscale brain image analysis: A review.

Computers in biology and medicine
Mesoscale microscopy images of the brain contain a wealth of information which can help us understand the working mechanisms of the brain. However, it is a challenging task to process and analyze these data because of the large size of the images, th...

Deep-learning-based cross-modality translation from Stokes image to bright-field contrast.

Journal of biomedical optics
SIGNIFICANCE: Mueller matrix (MM) microscopy has proven to be a powerful tool for probing microstructural characteristics of biological samples down to subwavelength scale. However, in clinical practice, doctors usually rely on bright-field microscop...

Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures.

Nature communications
Single-molecule experiments have changed the way we explore the physical world, yet data analysis remains time-consuming and prone to human bias. Here, we introduce Deep-LASI (Deep-Learning Assisted Single-molecule Imaging analysis), a software suite...

Facilitating cell segmentation with the projection-enhancement network.

Physical biology
Contemporary approaches to instance segmentation in cell science use 2D or 3D convolutional networks depending on the experiment and data structures. However, limitations in microscopy systems or efforts to prevent phototoxicity commonly require reco...

Validating instructional design and predicting student performance in histology education: Using machine learning via virtual microscopy.

Anatomical sciences education
As a part of modern technological environments, virtual microscopy enriches histological learning, with support from large institutional investments. However, existing literature does not supply empirical evidence of its role in improving pedagogy. V...

Dimensionality reduction for deep learning in infrared microscopy: a comparative computational survey.

The Analyst
While infrared microscopy provides molecular information at spatial resolution in a label-free manner, exploiting both spatial and molecular information for classifying the disease status of tissue samples constitutes a major challenge. One strategy ...

Deep learning-based photodamage reduction on harmonic generation microscope at low-level optical power.

Journal of biophotonics
The trade-off between high-quality images and cellular health in optical bioimaging is a crucial problem. We demonstrated a deep-learning-based power-enhancement (PE) model in a harmonic generation microscope (HGM), including second harmonic generati...