International journal of computer assisted radiology and surgery
Aug 9, 2016
PURPOSE: The staging of female breast cancer requires detailed information about the level of cancer spread through the lymphatic system. Common practice to obtain this information for patients with early-stage cancer is sentinel lymph node (SLN) bio...
This study aims to build a deep learning (DL) architecture for automated extraction of learned-from-data image features from the shear-wave elastography (SWE), and to evaluate the DL architecture in differentiation between benign and malignant breast...
Recent advances in machine learning yielded new techniques to train deep neural networks, which resulted in highly successful applications in many pattern recognition tasks such as object detection and speech recognition. In this paper we provide a h...
Several studies have successfully used molecular expression profiling in conjunction with classification techniques for discerning distinct disease groups. However, a majority of these studies do not provide sufficient insights into potential patient...
In association studies, the combined effects of single nucleotide polymorphism (SNP)-SNP interactions and the problem of imbalanced data between cases and controls are frequently ignored. In the present study, we used an improved multifactor dimensio...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jul 19, 2016
In this study we developed a graph based semi-supervised learning (SSL) scheme using deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a large amount of labeled data for training and fine tuning the parameters, an...
Medical diagnostics is often a multi-attribute problem, necessitating sophisticated tools for analyzing high-dimensional biomedical data. Mining this data often results in two crucial bottlenecks: 1) high dimensionality of features used to represent ...
Computer methods and programs in biomedicine
Jul 8, 2016
BACKGROUND AND OBJECTIVE: A large number of labeled medical image data is usually a requirement to train a well-performed computer-aided detection (CAD) system. But the process of data labeling is time consuming, and potential ethical and logistical ...
OBJECTIVE: To evaluate a system we developed that connects natural language processing (NLP) for information extraction from narrative text mammography reports with a Bayesian network for decision-support about breast cancer diagnosis. The ultimate g...
The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the proc...
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