Medical Ethics / Professional Responsibility

A study of deep learning methods for de-identification of clinical notes in cross-institute settings.

BACKGROUND: De-identification is a critical technology to facilitate the use of unstructured clinical text while protecting patient privacy and confidentiality. The clinical natural language processing (NLP) community has invested great efforts in developing methods and corpora for de-identification of clinical notes. These annotated corpora are valuable resources for developing automated systems ...

3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning methods.

Boosting is a family of supervised learning algorithm that convert a set of weak learners into a single strong one. It is popular in the field of object tracking, where its main purpose is to extract the position, motion, and trajectory from various features of interest within a sequence of video frames. A scientific application explored in this study is to combine the boosting tracker and the Hou...

Real-Time Visual Tracking with Variational Structure Attention Network.

Online training framework based on discriminative correlation filters for visual tracking has recently shown significant improvement in both accuracy ...

Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks.

It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distr...

Affinity and class probability-based fuzzy support vector machine for imbalanced data sets.

The learning problem from imbalanced data sets poses a major challenge in data mining community. Although conventional support vector machine can gene...

Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM.

Detection and quantification of functional deficits due to moderate traumatic brain injury (mTBI) is crucial for clinical decision-making and timely c...

The ethical, legal and social implications of using artificial intelligence systems in breast cancer care.

Breast cancer care is a leading area for development of artificial intelligence (AI), with applications including screening and diagnosis, risk calcul...

Generic Isolated Cell Image Generator.

Building automated cancer screening systems based on image analysis is currently a hot topic in computer vision and medical imaging community. One of ...

Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration.

Convolutional neural networks (CNNs) have recently led to significant advances in automatic segmentations of anatomical structures in medical images, ...

A segmentation method combining probability map and boundary based on multiple fully convolutional networks and repetitive training.

Cell nuclei image segmentation technology can help researchers observe each cell's stress response to drug treatment. However, it is still a challenge...

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

Automatic segmentation of prostate magnetic resonance (MR) images has great significance for the diagnosis and clinical application of prostate diseas...

Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation.

Accurate segmentation of the prostate from magnetic resonance (MR) images provides useful information for prostate cancer diagnosis and treatment. How...

A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images.

This paper addresses the task of nuclei segmentation in high-resolution histopathology images. We propose an automatic end-to-end deep neural network ...

Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks.

The Hausdorff Distance (HD) is widely used in evaluating medical image segmentation methods. However, the existing segmentation methods do not attempt...

Image Processing-Based Detection of Pipe Corrosion Using Texture Analysis and Metaheuristic-Optimized Machine Learning Approach.

To maintain the serviceability of buildings, the owners need to be informed about the current condition of the water supply and waste disposal systems...

Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network.

This paper presents a method for automatic breast pectoral muscle segmentation in mediolateral oblique mammograms using a Convolutional Neural Network...

The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis.

Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no system...

Locally linear SVMs based on boundary anchor points encoding.

In this paper, we propose a locally linear classifier based on boundary anchor points encoding (LLBAP) to achieve the efficiency of linear SVM and the...

MCRDR Knowledge-Based 3D Dialogue Simulation in Clinical Training and Assessment.

Dialogue-based simulation is a real-world practice technique for medical and clinical education that provides students with an opportunity to train us...

Smoothing dense spaces for improved relation extraction between drugs and adverse reactions.

BACKGROUND AND OBJECTIVE: This work aims at extracting Adverse Drug Reactions (ADRs), i.e. a harm directly caused by a drug at normal doses, from Elec...