Staffing & Scheduling

DeepSeg: A transfer-learning segmentation tool for limited sample training of nonhuman primate MRI.

Tissue segmentation of individual magnetic resonance imaging (MRI) is a fundamental step in building...

Integrating Artificial Intelligence Into Radiology Resident Training: A Call to Action.

Interobserver Agreement and Performance of Concurrent AI Assistance for Radiographic Evaluation of Knee Osteoarthritis.

Background Due to conflicting findings in the literature, there are concerns about a lack of objecti...

Impact of Transfer Learning Using Local Data on Performance of a Deep Learning Model for Screening Mammography.

Purpose To investigate the issues of generalizability and replication of deep learning models by ass...

Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review.

Purpose To investigate the accuracy and robustness of prostate segmentation using deep learning acro...

Effects of training with a rehabilitation device (Rebless®) on upper limb function in patients with chronic stroke: A randomized controlled trial.

BACKGROUND: Upper limb dysfunction is one of the most common sequelae of stroke and robotic therapy ...

Unsupervised Machine Learning in Countermovement Jump and Isometric Mid-Thigh Pull Performance Produces Distinct Combat and Physical Fitness Clusters in Male and Female U.S. Marine Corps Recruits.

INTRODUCTION: Several challenges face the U.S. Marine Corps (USMC) and other services in their effor...

Performance evaluation of ML models for preoperative prediction of HER2-low BC based on CE-CBBCT radiomic features: A prospective study.

To explore the value of machine learning (ML) models based on contrast-enhanced cone-beam breast com...

Development of an ontology for laparoscopic transabdominal adrenalectomy via a comprehensive modified Delphi survey and its validation on a multicentric pilot data set for surgical training and future video analysis with machine learning algorithms.

Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy.

UNLABELLED: Standard-of-care treatment regimens have long been designed for maximal cell killing, ye...

Convolutional neural network models applied to neuronal responses in macaque V1 reveal limited nonlinear processing.

Computational models of the primary visual cortex (V1) have suggested that V1 neurons behave like Ga...

Generative AI in glioma: Ensuring diversity in training image phenotypes to improve diagnostic performance for IDH mutation prediction.

BACKGROUND: This study evaluated whether generative artificial intelligence (AI)-based augmentation ...

Artificial Intelligence: Reflections on Its Use to Promote DEIA Principles for Residency Recruitment.

Artificial intelligence in histopathological image analysis of central nervous system tumours: A systematic review.

The convergence of digital pathology and artificial intelligence could assist histopathology image a...

Deep learning application to automated classification of recommendations made by hospital pharmacists during medication prescription review.

PURPOSE: Recommendations to improve therapeutics are proposals made by pharmacists during the prescr...

[Prediction of recurrence-free survival in lung adenocarcinoma based on self-supervised pre-training and multi-task learning].

Computed tomography (CT) imaging is a vital tool for the diagnosis and assessment of lung adenocarci...

Heterogeneous Forgetting Rates and Greedy Allocation in Slot-Based Memory Networks Promotes Signal Retention.

A key question in the neuroscience of memory encoding pertains to the mechanisms by which afferent s...

Emulating biological synaptic characteristics of HfOx/AlN-based 3D vertical resistive memory for neuromorphic systems.

Here, we demonstrate double-layer 3D vertical resistive random-access memory with a hole-type struct...

Advancements in oligometastatic breast cancer: a comprehensive review of current strategies and the role of artificial intelligence.

In the dynamic landscape of Breast Cancer (BC), Oligo- Metastatic Breast Cancer (OMBC) presents uniq...

Letter to the Editor Regarding "Targeting the Future: Developing a Training Curriculum for Robotic Assisted Neurosurgery".

Influence of training and expertise on deep neural network attention and human attention during a medical image classification task.

In many different domains, experts can make complex decisions after glancing very briefly at an imag...