AIMC Topic: Deep Learning

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DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation.

Nature communications
Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining he...

Research and Analysis of Brain Glioma Imaging Based on Deep Learning.

Journal of healthcare engineering
The incidence of glioma is increasing year by year, seriously endangering people's health. Magnetic resonance imaging (MRI) can effectively provide intracranial images of brain tumors and provide strong support for the diagnosis and treatment of the ...

Risk Factors of Restroke in Patients with Lacunar Cerebral Infarction Using Magnetic Resonance Imaging Image Features under Deep Learning Algorithm.

Contrast media & molecular imaging
This study was aimed to explore the magnetic resonance imaging (MRI) image features based on the fuzzy local information C-means clustering (FLICM) image segmentation method to analyze the risk factors of restroke in patients with lacunar infarction....

A Deep Learning-Based Text Classification of Adverse Nursing Events.

Journal of healthcare engineering
Adverse nursing events occur suddenly, unpredictably, or unexpectedly during course of clinical diagnosis and treatment processes in the hospitals. These events adversely affect the patient's diagnosis and treatment results and even increase the pati...

A disease network-based deep learning approach for characterizing melanoma.

International journal of cancer
Multiple types of genomic variations are present in cutaneous melanoma and some of the genomic features may have an impact on the prognosis of the disease. The access to genomics data via public repositories such as The Cancer Genome Atlas (TCGA) all...

A deep learning-based method for detecting and classifying the ultrasound images of suspicious thyroid nodules.

Medical physics
PURPOSE: The incidence of thyroid cancer has significantly increased in the last few decades. However, diagnosis of the thyroid nodules is labor and time intensive for radiologists and strongly depends on the personal experience of the radiologists. ...

Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging.

Nature communications
In current clinical practice, tumor response assessment is usually based on tumor size change on serial computerized tomography (CT) scan images. However, evaluation of tumor response to anti-vascular endothelial growth factor therapies in metastatic...

Deep learning-assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge.

European radiology
OBJECTIVES: To assess Prostate Imaging Reporting and Data System (PI-RADS)-trained deep learning (DL) algorithm performance and to investigate the effect of data size and prior knowledge on the detection of clinically significant prostate cancer (csP...

Development and validation of a supervised deep learning algorithm for automated whole-slide programmed death-ligand 1 tumour proportion score assessment in non-small cell lung cancer.

Histopathology
AIMS: Immunohistochemical programmed death-ligand 1 (PD-L1) staining to predict responsiveness to immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) has several drawbacks: a robust gold standard is lacking, and there is substa...

Automated Whole-Liver MRI Segmentation to Assess Steatosis and Iron Quantification in Chronic Liver Disease.

Radiology
Background Standardized manual region of interest (ROI) sampling strategies for hepatic MRI steatosis and iron quantification are time consuming, with variable results. Purpose To evaluate the performance of automatic MRI whole-liver segmentation (WL...