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Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology.

Medical image analysis
Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift between laboratories, convolutional neural networks (CNNs) trained with...

Encoder-decoder with dense dilated spatial pyramid pooling for prostate MR images segmentation.

Computer assisted surgery (Abingdon, England)
Automatic segmentation of prostate magnetic resonance (MR) images has great significance for the diagnosis and clinical application of prostate diseases. It faces enormous challenges because of the low contrast of the tissue boundary and the small ef...

Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation.

Journal of medicinal chemistry
The kinome-wide virtual profiling of small molecules with high-dimensional structure-activity data is a challenging task in drug discovery. Here, we present a virtual profiling model against a panel of 391 kinases based on large-scale bioactivity dat...

Unsupervised method for normalization of hematoxylin-eosin stain in histological images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Histological images stained with hematoxylin-eosin are widely used by pathologists for cancer diagnosis. However, these images can have color variations that highly influence the histological image processing techniques. To deal with this potential l...

Multiscale brain MRI super-resolution using deep 3D convolutional networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The purpose of super-resolution approaches is to overcome the hardware limitations and the clinical requirements of imaging procedures by reconstructing high-resolution images from low-resolution acquisitions using post-processing methods. Super-reso...

Physiological indices of challenge and threat: A data-driven investigation of autonomic nervous system reactivity during an active coping stressor task.

Psychophysiology
We utilized a data-driven, unsupervised machine learning approach to examine patterns of peripheral physiological responses during a motivated performance context across two large, independent data sets, each with multiple peripheral physiological me...

Clustering-based undersampling with random over sampling examples and support vector machine for imbalanced classification of breast cancer diagnosis.

Computer assisted surgery (Abingdon, England)
To overcome the two-class imbalanced classification problem existing in the diagnosis of breast cancer, a hybrid of Random Over Sampling Example, K-means and Support vector machine (RK-SVM) model is proposed which is based on sample selection. Random...

An investigation of CNN models for differentiating malignant from benign lesions using small pathologically proven datasets.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cancer has been one of the most threatening diseases to human health. There have been many efforts devoted to the advancement of radiology and transformative tools (e.g. non-invasive computed tomographic or CT imaging) to detect cancer in early stage...

Accurate automated Cobb angles estimation using multi-view extrapolation net.

Medical image analysis
Accurate automated quantitative Cobb angle estimation that quantitatively evaluates scoliosis plays an important role in scoliosis diagnosis and treatment. It solves the problem of the traditional manual method, which is the current clinical standard...

Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment.

Skeletal radiology
OBJECTIVE: To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segme...