Medical Ethics / Professional Responsibility

A deep learning model incorporating part of speech and self-matching attention for named entity recognition of Chinese electronic medical records.

BACKGROUND: The Named Entity Recognition (NER) task as a key step in the extraction of health information, has encountered many challenges in Chinese Electronic Medical Records (EMRs). Firstly, the casual use of Chinese abbreviations and doctors' personal style may result in multiple expressions of the same entity, and we lack a common Chinese medical dictionary to perform accurate entity extracti...

Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology.

Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of me...

CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.

Accurate segmentation of the prostate and organs at risk (e.g., bladder and rectum) in CT images is a crucial step for radiation therapy in the treatm...

Modeling second-order boundary perception: A machine learning approach.

Visual pattern detection and discrimination are essential first steps for scene analysis. Numerous human psychophysical studies have modeled visual pa...

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., vary...

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 syste...

'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...