AIMC Topic: Deep Learning

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Rapid video-based deep learning of cognate versus non-cognate T cell-dendritic cell interactions.

Scientific reports
Identification of cognate interactions between antigen-specific T cells and dendritic cells (DCs) is essential to understanding immunity and tolerance, and for developing therapies for cancer and autoimmune diseases. Conventional techniques for selec...

Abstractive Arabic Text Summarization Based on Deep Learning.

Computational intelligence and neuroscience
Text summarization (TS) is considered one of the most difficult tasks in natural language processing (NLP). It is one of the most important challenges that stand against the modern computer system's capabilities with all its new improvement. Many pap...

Revealing Fine Structures of the Retinal Receptive Field by Deep-Learning Networks.

IEEE transactions on cybernetics
Deep convolutional neural networks (CNNs) have demonstrated impressive performance on many visual tasks. Recently, they became useful models for the visual system in neuroscience. However, it is still not clear what is learned by CNNs in terms of neu...

QPoweredCompound2DeNovoDrugPropMax - a novel programmatic tool incorporating deep learning and methods for automated in silico bio-activity discovery for any compound of interest.

Journal of biomolecular structure & dynamics
Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction have been shown in the area of social networks through which highly custo...

Impact of Deep Learning Reconstruction Combined With a Sharpening Filter on Single-Shot Fast Spin-Echo T2-Weighted Magnetic Resonance Imaging of the Uterus.

Investigative radiology
OBJECTIVE: This study aimed to evaluate the effects of deep learning (DL) reconstruction and a postprocessing sharpening filter on the image quality of single-shot fast spin-echo (SSFSE) T2-weighted imaging (T2WI) of the uterus.

Deep learning prediction of sex on chest radiographs: a potential contributor to biased algorithms.

Emergency radiology
BACKGROUND: Deep convolutional neural networks (DCNNs) for diagnosis of disease on chest radiographs (CXR) have been shown to be biased against males or females if the datasets used to train them have unbalanced sex representation. Prior work has sug...

Bone suppression on pediatric chest radiographs via a deep learning-based cascade model.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Bone suppression images (BSIs) of chest radiographs (CXRs) have been proven to improve diagnosis of pulmonary diseases. To acquire BSIs, dual-energy subtraction (DES) or a deep-learning-based model trained with DES-based BSI...

Deep Learning of Morphologic Correlations To Accurately Classify CD4+ and CD8+ T Cells by Diffraction Imaging Flow Cytometry.

Analytical chemistry
The two major subtypes of human T cells, CD4+ and CD8+, play important roles in adaptive immune response by their diverse functions. To understand the structure-function relation at the single cell level, we isolated 2483 CD4+ and 2450 CD8+ T cells f...

A Study on 3D Deep Learning-Based Automatic Diagnosis of Nasal Fractures.

Sensors (Basel, Switzerland)
This paper reported a study on the 3-dimensional deep-learning-based automatic diagnosis of nasal fractures. (1) Background: The nasal bone is the most protuberant feature of the face; therefore, it is highly vulnerable to facial trauma and its fract...