AIMC Topic: ROC Curve
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Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection.
Computers in biology and medicine
Apr 18, 2017
Different types of breast cancer are affecting lives of women across the world. Common types include Ductal carcinoma in situ (DCIS), Invasive ductal carcinoma (IDC), Tubular carcinoma, Medullary carcinoma, and Invasive lobular carcinoma (ILC). While...
Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images.
Medical physics
Apr 17, 2017
PURPOSE: It is very important for calculation of clinical indices and diagnosis to detect thyroid nodules from ultrasound images. However, this task is a challenge mainly due to heterogeneous thyroid nodules with distinct components are similar to ba...
Driver behavior profiling: An investigation with different smartphone sensors and machine learning.
PloS one
Apr 10, 2017
Driver behavior impacts traffic safety, fuel/energy consumption and gas emissions. Driver behavior profiling tries to understand and positively impact driver behavior. Usually driver behavior profiling tasks involve automated collection of driving da...
Use of a machine learning framework to predict substance use disorder treatment success.
PloS one
Apr 10, 2017
There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitat...
Comparison of Machine Learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients.
IEEE/ACM transactions on computational biology and bioinformatics
Apr 4, 2017
BACKGROUND/AIM: Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy. This study aims to evaluate different machine learning ...
Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer.
European radiology
Apr 3, 2017
OBJECTIVE: To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa).
Assessment of Semantic Similarity between Proteins Using Information Content and Topological Properties of the Gene Ontology Graph.
IEEE/ACM transactions on computational biology and bioinformatics
Mar 31, 2017
The semantic similarity between two interacting proteins can be estimated by combining the similarity scores of the GO terms associated with the proteins. Greater number of similar GO annotations between two proteins indicates greater interaction aff...
Discriminating cognitive status in Parkinson's disease through functional connectomics and machine learning.
Scientific reports
Mar 28, 2017
There is growing interest in the potential of neuroimaging to help develop non-invasive biomarkers in neurodegenerative diseases. In this study, connection-wise patterns of functional connectivity were used to distinguish Parkinson's disease patients...
Automated Identification of Diabetic Retinopathy Using Deep Learning.
Ophthalmology
Mar 27, 2017
PURPOSE: Diabetic retinopathy (DR) is one of the leading causes of preventable blindness globally. Performing retinal screening examinations on all diabetic patients is an unmet need, and there are many undiagnosed and untreated cases of DR. The obje...
A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks.
Computers in biology and medicine
Mar 27, 2017
Spinal metastasis, a metastatic cancer of the spine, is the most common malignant disease in the spine. In this study, we investigate the feasibility of automated spinal metastasis detection in magnetic resonance imaging (MRI) by using deep learning ...
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