AIMC Topic: Microscopy

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Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy.

The Journal of clinical investigation Feb 3, 2020
Increases in the number of cell therapies in the preclinical and clinical phases have prompted the need for reliable and noninvasive assays to validate transplant function in clinical biomanufacturing. We developed a robust characterization methodolo...
Deep Learning Image Processing, Computer-Assisted Humans Retinal Pigment Epithelium Induced Pluripotent Stem Cells Cell Differentiation Microscopy
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In vitro monitoring of photoinduced necrosis in HeLa cells using digital holographic microscopy and machine learning.

Journal of the Optical Society of America. A, Optics, image science, and vision Feb 1, 2020
Digital holographic microscopy supplemented with the developed cell segmentation and machine learning and classification algorithms is implemented for quantitative description of the dynamics of cellular necrosis induced by photodynamic treatment in ...
Holography Cell Survival Microscopy HeLa Cells Necrosis Machine Learning Humans Image Processing, Computer-Assisted
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[Artificial intelligence (AI) and hematological diseases: establishment of a peripheral blood convolutional neural network (CNN)-based digital morphology analysis system].

[Rinsho ketsueki] The Japanese journal of clinical hematology Jan 1, 2020
Morphological analysis of the blood smear is an essential element of diagnosing a disease hematologically and has been performed by conventional manual light microscopy for several decades. Although this method is the gold standard, it is labor-inten...
Hematologic Tests Neural Networks, Computer Artificial Intelligence Microscopy Hematologic Diseases Humans
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Deep representation learning for domain adaptable classification of infrared spectral imaging data.

Bioinformatics (Oxford, England) Jan 1, 2020
MOTIVATION: Applying infrared microscopy in the context of tissue diagnostics heavily relies on computationally preprocessing the infrared pixel spectra that constitute an infrared microscopic image. Existing approaches involve physical models, which...
Deep Learning Imaging, Three-Dimensional Neural Networks, Computer Microscopy Spectrophotometry, Infrared Computational Biology Models, Theoretical Pathology
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THE PROJECT OF ANOTHER LOW-COST METAPHASE FINDER (SECOND REPORT-APPLICATION OF ARTIFICIAL INTELLIGENCE).

Radiation protection dosimetry Dec 31, 2019
Biological dosimetry is used to estimate individual absorbed radiation dose by quantifying an appropriate biological marker. The most popular gold-standard marker is the appearance of dicentric chromosomes in metaphase. The metaphase finder is a tool...
Metaphase Chromosome Aberrations Cytogenetic Analysis Software Microscopy Humans Radiation Dosage Lymphocytes Artificial Intelligence
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Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images.

Journal of biomedical optics Dec 1, 2019
We study a problem scenario of super-resolution (SR) algorithms in the context of whole slide imaging (WSI), a popular imaging modality in digital pathology. Instead of just one pair of high- and low-resolution images, which is typically the setup in...
Image Interpretation, Computer-Assisted Humans Histological Techniques Neural Networks, Computer Neoplasms Machine Learning Microscopy Algorithms
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Deep learning gets scope time.

Nature methods Dec 1, 2019
Microscopy Humans Deep Learning
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Juan C. Caicedo.

Nature methods Dec 1, 2019
Humans Machine Learning Algorithms Image Processing, Computer-Assisted Microscopy
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Rotation equivariant and invariant neural networks for microscopy image analysis.

Bioinformatics (Oxford, England) Jul 15, 2019
MOTIVATION: Neural networks have been widely used to analyze high-throughput microscopy images. However, the performance of neural networks can be significantly improved by encoding known invariance for particular tasks. Highly relevant to the goal o...
Rotation Microscopy Neural Networks, Computer Software
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Human Induced Pluripotent Stem Cell Reprogramming Prediction in Microscopy Images using LSTM based RNN.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
We present a LSTM (Long Short-Term Memory) based RNN (recurrent neural network) method for predicting human induced Pluripotent Stem (hiPS) cells in the reprogramming process. The method uses a trained LSTM network by time-lapse microscopy images to ...
Microscopy Neural Networks, Computer Induced Pluripotent Stem Cells Humans Cellular Reprogramming
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