Medical Ethics / Professional Responsibility

Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection.

Cell nuclei detection is a challenging research topic because of limitations in cellular image quality and diversity of nuclear morphology, i.e., varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduring interest with promising recent success shown by deep learning methods. These methods train convolutional neural networks (CNNs) with a training set...

Disease vocabulary size as a surrogate marker for physicians' disease knowledge volume.

OBJECTIVE: Recognizing what physicians know and do not know about a particular disease is one of the keys to designing clinical decision support systems, since these systems can fulfill complementary role by recognizing this boundary. To our knowledge, however, no study has attempted to quantify how many diseases physicians actually know and thus the boundary is unclear. This study explores a meth...

'Artiphysiology' reveals V4-like shape tuning in a deep network trained for image classification.

Deep networks provide a potentially rich interconnection between neuroscientific and artificial approaches to understanding visual intelligence, but t...

Mixture Model Segmentation System for Parasagittal Meningioma brain Tumor Classification based on Hybrid Feature Vector.

Meningioma is the one of the most common type of brain tumor, it as arises from the meninges and encloses the spine and the brain inside the skull. It...

Dense Associative Memory Is Robust to Adversarial Inputs.

Deep neural networks (DNNs) trained in a supervised way suffer from two known problems. First, the minima of the objective function used in learning c...

Modeling growth limits of Bacillus spp. spores by using deep-learning algorithm.

Growth/no growth boundary models for Bacillus spores that accounted for the effects of environmental pH, water activity (a), acetic acid, lactic acid,...

Convolutional Neural Network for Segmentation and Measurement of Intima Media Thickness.

The measurement of Carotid Intima Media Thickness (IMT) on Common Carotid Artery (CCA) is a principle marker of risk of cardiovascular disease. This p...

RACE-Net: A Recurrent Neural Network for Biomedical Image Segmentation.

The level set based deformable models (LDM) are commonly used for medical image segmentation. However, they rely on a handcrafted curve evolution velo...

3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks.

In this paper, we present a novel approach for 3D dental model segmentation via deep Convolutional Neural Networks (CNNs). Traditional geometry-based ...

Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation.

Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practical...

Robot Assisted Surgical Ward Rounds: Virtually Always There.

 While an explosion in technological sophistication has revolutionized surgery within the operating theatre, delivery of surgical ward-based care has ...

Protecting Your Patients' Interests in the Era of Big Data, Artificial Intelligence, and Predictive Analytics.

The Hippocratic oath and the Belmont report articulate foundational principles for how physicians interact with patients and research subjects. The in...

Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation.

Accurate and automatic organ segmentation from 3D radiological scans is an important yet challenging problem for medical image analysis. Specifically,...

Breast lesion classification based on supersonic shear-wave elastography and automated lesion segmentation from B-mode ultrasound images.

Supersonic shear-wave elastography (SWE) has emerged as a useful imaging modality for breast lesion assessment. Regions of interest (ROIs) were requir...

A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis.

This paper proposes a novel Adaptive Region-based Edge Smoothing Model (ARESM) for automatic boundary detection of optic disc and cup to aid automatic...

Post-boosting of classification boundary for imbalanced data using geometric mean.

In this paper, a novel imbalance learning method for binary classes is proposed, named as Post-Boosting of classification boundary for Imbalanced data...

Evaluating the impact of vitreomacular adhesion on anti-VEGF therapy for retinal vein occlusion using machine learning.

Vitreomacular adhesion (VMA) represents a prognostic biomarker in the management of exudative macular disease using anti-vascular endothelial growth f...

Synchronization of stochastic reaction-diffusion neural networks with Dirichlet boundary conditions and unbounded delays.

In this paper, synchronization of stochastic reaction-diffusion neural networks with Dirichlet boundary conditions and unbounded discrete time-varying...

Fuzzy spectral clustering for automated delineation of chronic wound region using digital images.

Chronic wound is an abnormal disease condition of localized injury to the skin and its underlying tissues having physiological impaired healing respon...

Synchronization of Reaction-Diffusion Neural Networks With Dirichlet Boundary Conditions and Infinite Delays.

This paper is concerned with synchronization for a class of reaction-diffusion neural networks with Dirichlet boundary conditions and infinite discret...