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Artificial neural network approach for predicting blood brain barrier permeability based on a group contribution method.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The purpose of this study was to develop a quantitative structure-activity relationship (QSAR) model for the prediction of blood brain barrier (BBB) permeability by using artificial neural networks (ANN) in combination with ...

A survey of deep learning models in medical therapeutic areas.

Artificial intelligence in medicine
Artificial intelligence is a broad field that comprises a wide range of techniques, where deep learning is presently the one with the most impact. Moreover, the medical field is an area where data both complex and massive and the importance of the de...

Choroid Segmentation of Retinal OCT Images Based on CNN Classifier and - Fitter.

Computational and mathematical methods in medicine
Optical coherence tomography (OCT) is a noninvasive cross-sectional imaging technology used to examine the retinal structure and pathology of the eye. Evaluating the thickness of the choroid using OCT images is of great interests for clinicians and r...

A neurodynamic optimization approach to supervised feature selection via fractional programming.

Neural networks : the official journal of the International Neural Network Society
Feature selection is an important issue in machine learning and data mining. Most existing feature selection methods are greedy in nature thus are prone to sub-optimality. Though some global feature selection methods based on unsupervised redundancy ...

Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases.

NeuroImage
Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are usually performed manually. We present Multi-task U-Net (MU-Net), a convolutional neural network de...

Classification of malignant tumours in breast ultrasound using unsupervised machine learning approaches.

Scientific reports
Traditional computer-aided diagnosis (CAD) processes include feature extraction, selection, and classification. Effective feature extraction in CAD is important in improving the classification's performance. We introduce a machine-learning method and...

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...

Machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer.

Scientific reports
To construct a machine learning algorithm model of lymph node metastasis (LNM) in patients with poorly differentiated-type intramucosal gastric cancer. 1169 patients with postoperative gastric cancer were divided into a training group and a test grou...

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...

Automatic detection and segmentation of multiple brain metastases on magnetic resonance image using asymmetric UNet architecture.

Physics in medicine and biology
Detection of brain metastases is a paramount task in cancer management due both to the number of high-risk patients and the difficulty of achieving consistent detection. In this study, we aim to improve the accuracy of automated brain metastasis (BM)...