AIMC Topic: Deep Learning

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Classification of malignant lung cancer using deep learning.

Journal of medical engineering & technology
In the automatic detection of suspicious shaded regions on CT images derived from the LIDC-IDRI dataset, the diagnostic system plays a significant role. This paper introduces an automatic recognition method for lung nodules of the regions of concern ...

Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Axonal loss denervates muscle, leading to an increase of fat accumulation in the muscle. Therefore, fat fraction (FF) in whole limb muscle using MRI has emerged as a monitoring biomarker for axonal loss in patients with peripheral neuropa...

Deep learning magnetic resonance spectroscopy fingerprints of brain tumours using quantum mechanically synthesised data.

NMR in biomedicine
Metabolic fingerprints are valuable biomarkers for diseases that are associated with metabolic disorders. 1H magnetic resonance spectroscopy (MRS) is a unique noninvasive diagnostic tool that can depict the metabolic fingerprint based solely on the p...

Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images.

Scientific reports
As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past decade. On the other hand, recent advances in deep learning and transfer learning have shown significant potential in the quanti...

Deep learning identifies partially overlapping subnetworks in the human social brain.

Communications biology
Complex social interplay is a defining property of the human species. In social neuroscience, many experiments have sought to first define and then locate 'perspective taking', 'empathy', and other psychological concepts to specific brain circuits. S...

Deep learning-based automatic delineation of the hippocampus by MRI: geometric and dosimetric evaluation.

Radiation oncology (London, England)
BACKGROUND: Whole brain radiotherapy (WBRT) can impair patients' cognitive function. Hippocampal avoidance during WBRT can potentially prevent this side effect. However, manually delineating the target area is time-consuming and difficult. Here, we p...

Automatic segmentation of white matter hyperintensities from brain magnetic resonance images in the era of deep learning and big data - A systematic review.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: White matter hyperintensities (WMH), of presumed vascular origin, are visible and quantifiable neuroradiological markers of brain parenchymal change. These changes may range from damage secondary to inflammation and other neurological con...

Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide. In coping with it, histopathology image analysis (HIA) provides key information for clinical diagnosis of CRC. Nowadays, the deep learning methods are widely u...

Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring.

Sensors (Basel, Switzerland)
The differential count of white blood cells (WBCs) is one widely used approach to assess the status of a patient's immune system. Currently, the main methods of differential WBC counting are manual counting and automatic instrument analysis with labe...

Deep learning based prediction of prognosis in nonmetastatic clear cell renal cell carcinoma.

Scientific reports
Survival analyses for malignancies, including renal cell carcinoma (RCC), have primarily been conducted using the Cox proportional hazards (CPH) model. We compared the random survival forest (RSF) and DeepSurv models with the CPH model to predict rec...