AIMC Topic:
Image Interpretation, Computer-Assisted
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Automated identification of Myxobacterial genera using Convolutional Neural Network.
Scientific reports
Dec 3, 2019
The Myxococcales order consist of eleven families comprising30 genera, and are featured by the formation of the highest level of differential structure aggregations called fruiting bodies. These multicellular structures are essential for their resist...
A novel computer-aided diagnosis system for the early detection of hypertension based on cerebrovascular alterations.
NeuroImage. Clinical
Dec 2, 2019
Hypertension is a leading cause of mortality in the USA. While simple tools such as the sphygmomanometer are widely used to diagnose hypertension, they could not predict the disease before its onset. Clinical studies suggest that alterations in the s...
A novel hybrid atlas-free hierarchical graph-based segmentation of newborn brain MRI using wavelet filter banks.
The International journal of neuroscience
Dec 1, 2019
: The newborn brain MRI (magnetic resonance imaging) tissue segmentation plays a vital part in assessment of primary brain growth. In the newborn stage (nearly less than 28 days old), in T1- as well as T2-weighted MR images similar levels of intensit...
An overview of deep learning algorithms and water exchange in colonoscopy in improving adenoma detection.
Expert review of gastroenterology & hepatology
Nov 30, 2019
: Among the Gastrointestinal (GI) Endoscopy Editorial Board top 10 topics in advances in endoscopy in 2018, water exchange colonoscopy and artificial intelligence were both considered important advances. Artificial intelligence holds the potential to...
Joint Prediction of Breast Cancer Histological Grade and Ki-67 Expression Level Based on DCE-MRI and DWI Radiomics.
IEEE journal of biomedical and health informatics
Nov 28, 2019
OBJECTIVE: Histologic grade and Ki-67 proliferation status are important clinical indictors for breast cancer prognosis and treatment. The purpose of this study is to improve prediction accuracy of these clinical indicators based on tumor radiomic an...
Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study.
Gut
Nov 28, 2019
OBJECTIVE: Diagnostic tests, such as Immunoscore, predict prognosis in patients with colon cancer. However, additional prognostic markers could be detected on pathological slides using artificial intelligence tools.
Diagnosis of Thyroid Nodules: Performance of a Deep Learning Convolutional Neural Network Model vs. Radiologists.
Scientific reports
Nov 28, 2019
Computer-aided diagnosis (CAD) systems hold potential to improve the diagnostic accuracy of thyroid ultrasound (US). We aimed to develop a deep learning-based US CAD system (dCAD) for the diagnosis of thyroid nodules and compare its performance with ...
Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Nov 27, 2019
We present the application of limited one-time sampling irregularity map (LOTS-IM): a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), for quantitatively assessing white matter hyperinte...
Development of a Deep Learning Algorithm for the Histopathologic Diagnosis and Gleason Grading of Prostate Cancer Biopsies: A Pilot Study.
European urology focus
Nov 22, 2019
BACKGROUND: The pathologic diagnosis and Gleason grading of prostate cancer are time-consuming, error-prone, and subject to interobserver variability. Machine learning offers opportunities to improve the diagnosis, risk stratification, and prognostic...
Ophthalmic diagnosis using deep learning with fundus images - A critical review.
Artificial intelligence in medicine
Nov 22, 2019
An overview of the applications of deep learning for ophthalmic diagnosis using retinal fundus images is presented. We describe various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation...
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