AIMC Topic: Deep Learning

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AI-driven deep convolutional neural networks for chest X-ray pathology identification.

Journal of X-ray science and technology Jan 1, 2022
BACKGROUND: Chest X-ray images are widely used to detect many different lung diseases. However, reading chest X-ray images to accurately detect and classify different lung diseases by doctors is often difficult with large inter-reader variability. Th...
Deep Learning X-Rays Humans COVID-19 Neural Networks, Computer Algorithms SARS-CoV-2
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GRNUlar: A Deep Learning Framework for Recovering Single-Cell Gene Regulatory Networks.

Journal of computational biology : a journal of computational molecular cell biology Jan 1, 2022
We propose GRNUlar, a novel deep learning framework for supervised learning of gene regulatory networks (GRNs) from single-cell RNA-Sequencing (scRNA-Seq) data. Our framework incorporates two intertwined models. First, we leverage the expressive abil...
Databases, Nucleic Acid Supervised Machine Learning Saccharomyces cerevisiae Deep Learning Neural Networks, Computer Single-Cell Analysis Computational Biology Algorithms Humans RNA-Seq Escherichia coli Computer Simulation Bias Animals Gene Regulatory Networks Mice
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[Evaluation of Radiograph Accuracy in Skull X-ray Images Using Deep Learning].

Nihon Hoshasen Gijutsu Gakkai zasshi Jan 1, 2022
PURPOSE: Accurate positioning is essential for radiography, and it is especially important to maintain image reproducibility in follow-up observations. The decision on re-taking radiographs is entrusting to the individual radiological technologist. T...
Radiography Skull X-Rays Reproducibility of Results Deep Learning
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[Improvement of Motion Artifacts in Brain MRI Using Deep Learning by Simulation Training Data].

Nihon Hoshasen Gijutsu Gakkai zasshi Jan 1, 2022
PURPOSE: To test whether deep learning can be used to effectively reduce artifacts in MR images of the brain.
Signal-To-Noise Ratio Humans Simulation Training Brain Image Processing, Computer-Assisted Computer Simulation Magnetic Resonance Imaging Deep Learning Artifacts
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Deep-learning approach for automated thickness measurement of epithelial tissue and scab using optical coherence tomography.

Journal of biomedical optics Jan 1, 2022
SIGNIFICANCE: In order to elucidate therapeutic treatment to accelerate wound healing, it is crucial to understand the process underlying skin wound healing, especially re-epithelialization. Epidermis and scab detection is of importance in the wound ...
Tomography, Optical Coherence Epidermis Cross-Sectional Studies Deep Learning Neural Networks, Computer
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Detection and Weak Segmentation of Masses in Gray-Scale Breast Mammogram Images Using Deep Learning.

Yonsei medical journal Jan 1, 2022
PURPOSE: In this paper, we propose deep-learning methodology with which to enhance the mass differentiation performance of convolutional neural network (CNN)-based architecture.
Breast Neoplasms Deep Learning Mammography Cell Differentiation ROC Curve Humans Neural Networks, Computer Female
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Demographic Reporting in Publicly Available Chest Radiograph Data Sets: Opportunities for Mitigating Sex and Racial Disparities in Deep Learning Models.

Journal of the American College of Radiology : JACR Jan 1, 2022
OBJECTIVE: Data sets with demographic imbalances can introduce bias in deep learning models and potentially amplify existing health disparities. We evaluated the reporting of demographics and potential biases in publicly available chest radiograph (C...
Male Deep Learning Radiography Humans Female Bias Ethnicity
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Detecting Racial/Ethnic Health Disparities Using Deep Learning From Frontal Chest Radiography.

Journal of the American College of Radiology : JACR Jan 1, 2022
PURPOSE: The aim of this study was to assess racial/ethnic and socioeconomic disparities in the difference between atherosclerotic vascular disease prevalence measured by a multitask convolutional neural network (CNN) deep learning model using fronta...
Humans COVID-19 Retrospective Studies Ethnicity Social Deprivation Liver Neoplasms SARS-CoV-2 Carcinoma, Hepatocellular Deep Learning Radiography
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Interpretable deep learning approach for oral cancer classification using guided attention inference network.

Journal of biomedical optics Jan 1, 2022
SIGNIFICANCE: Convolutional neural networks (CNNs) show the potential for automated classification of different cancer lesions. However, their lack of interpretability and explainability makes CNNs less than understandable. Furthermore, CNNs may inco...
Attention Deep Learning Mouth Neoplasms Neural Networks, Computer Reproducibility of Results Humans
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Deep learning and protein structure modeling.

Nature methods Jan 1, 2022
Algorithms Protein Conformation Deep Learning Proteins Sequence Alignment Models, Molecular Computational Biology Multiprotein Complexes
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