AIMC Topic: Deep Learning

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Highly Flexible Deep-Learning-Based Automatic Analysis for Graphically Encoded Hydrogel Microparticles.

ACS sensors
Graphically encoded hydrogel microparticle (HMP)-based bioassay is a diagnostic tool characterized by exceptional multiplex detectability and robust sensitivity and specificity. Specifically, deep learning enables highly fast and accurate analyses of...

Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality.

EBioMedicine
BACKGROUND: Colorectal cancers are the fourth most diagnosed cancer and the second leading cancer in number of deaths. Many clinical variables, pathological features, and genomic signatures are associated with patient risk, but reliable patient strat...

A Customized Deep Sleep Recommender System Using Hybrid Deep Learning.

Sensors (Basel, Switzerland)
This paper proposes a recommendation system based on a hybrid learning approach for a personal deep sleep service, called the Customized Deep Sleep Recommender System (CDSRS). Sleep is one of the most important factors for human life in modern societ...

Biophysical Profiling of Sickle Cell Disease Using Holographic Cytometry and Deep Learning.

International journal of molecular sciences
Sickle cell disease (SCD) is an inherited hematological disorder associated with high mortality rates, particularly in sub-Saharan Africa. SCD arises due to the polymerization of sickle hemoglobin, which reduces flexibility of red blood cells (RBCs),...

Deep learning-based diagnosis of histopathological patterns for invasive non-mucinous lung adenocarcinoma using semantic segmentation.

BMJ open
OBJECTIVES: The application of artificial intelligence (AI) to the field of pathology has facilitated the development of digital pathology, hence, making AI-assisted diagnosis possible. Due to the variety of lung cancers and the subjectivity of manua...

Deep learning-based multiomics integration model for predicting axillary lymph node metastasis in breast cancer.

Future oncology (London, England)
To develop a deep learning-based multiomics integration model. Five types of omics data (mRNA, DNA methylation, miRNA, copy number variation and protein expression) were used to build a deep learning-based multiomics integration model a deep neura...

Improving protein structure prediction with extended sequence similarity searches and deep-learning-based refinement in CASP15.

Proteins
The human predictor team PEZYFoldings got first place with the assessor's formulae (3rd place with Global Distance Test Total Score [GDT-TS]) in the single-domain category and 10th place in the multimer category in Critical Assessment of Structure Pr...

Automatic Myocardial Contrast Echocardiography Image Quality Assessment Using Deep Learning: Impact on Myocardial Perfusion Evaluation.

Ultrasound in medicine & biology
OBJECTIVE: The image quality of myocardial contrast echocardiography (MCE) is critical for precise myocardial perfusion evaluation but challenging for echocardiographers. Differences in quality may lead to diagnostic heterogeneity. This study was aim...

Novel fuzzy deep learning approach for automated detection of useful COVID-19 tweets.

Artificial intelligence in medicine
Coronavirus (COVID-19) is a newly discovered viral disease from the SARS-CoV-2 family. This has caused a moral panic resulting in the spread of informative and uninformative information about COVID-19 and its effects. Twitter is a popular social medi...