Medical Ethics / Professional Responsibility

DCATNet: polyp segmentation with deformable convolution and contextual-aware attention network.

Polyp segmentation is crucial in computer-aided diagnosis but remains challenging due to the complexity of medical images and anatomical variations. Current state-of-the-art methods struggle with accurate polyp segmentation due to the variability in size, shape, and texture. These factors make boundary detection challenging, often resulting in incomplete or inaccurate segmentation. To address thes...

Enhancing cell instance segmentation in scanning electron microscopy images via a deep contour closing operator.

Accurately segmenting and individualizing cells in scanning electron microscopy (SEM) images is a highly promising technique for elucidating tissue architecture in oncology. While current artificial intelligence (AI)-based methods are effective, errors persist, necessitating time-consuming manual corrections, particularly in areas where the quality of cell contours in the image is poor and require...

Hot Injection Assisted Electronically Modulated Twin and Grain Boundary Rich Sub-2 nm PtCo Alloy Resistant to Phosphate Ion for PEMFCs.

Modulation of the electronic d-band center, structural defects (line defects), and particle size of PtCo alloy electrocatalyst have huge significance ...

Design and Development of a Public AI Referee Assistance System Based on Harmony OS Platform.

The Hawkeye system was regarded as a successful and effective referee assistant in commercial applications. However, the high hardware investment and ...

Role of physics-informed constraints in real-time estimation of 3D vascular fluid dynamics using multi-case neural network.

Numerical simulations of fluid dynamics in tube-like structures are important to biomedical research to model flow in blood vessels and airways. It is...

Modelling fourth-order hyperelasticity in soft solids using physics informed neural networks without labelled data.

Mild traumatic brain injury can result from shear shock wave formation in the brain in the event of a head impact like in contact sports, road traffic...

A hybrid object detection approach for visually impaired persons using pigeon-inspired optimization and deep learning models.

Visually challenged persons include a significant part of the population, and they exist all over the globe. Recently, technology has demonstrated its...

Low-cost sensor-based algal bloom labeling: a comparative study of SVM and logic methods.

This study explores a low-cost sensor system for real-time algae bloom detection and water management. Harmful algal blooms (HABs) threaten water qual...

Boundary-enhanced local-global collaborative network for medical image segmentation.

Medical imaging plays a vital role as an auxiliary tool in clinical diagnosis and treatment, with segmentation serving as a crucial foundational proce...

Effective Semi-Supervised Medical Image Segmentation With Probabilistic Representations and Prototype Learning.

Label scarcity, class imbalance and data uncertainty are three primary challenges that are commonly encountered in the semi-supervised medical image s...

Deep-Learning Potential Molecular Dynamics Study on Nanopolycrystalline Al-Er Alloys: Effects of Er Concentration, Grain Boundary Segregation, and Grain Size on Plastic Deformation.

Understanding the tensile mechanical properties of Al-Er alloys at the atomic scale is essential, and molecular dynamics (MD) simulations offer valuab...

Comparative analysis of deep learning architectures for breast region segmentation with a novel breast boundary proposal.

Segmentation of the breast region in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for the automatic measurement of brea...

Segment Like A Doctor: Learning reliable clinical thinking and experience for pancreas and pancreatic cancer segmentation.

Pancreatic cancer is a lethal invasive tumor with one of the worst prognosis. Accurate and reliable segmentation for pancreas and pancreatic cancer on...

Vision Mamba and xLSTM-UNet for medical image segmentation.

Deep learning-based medical image segmentation methods are generally divided into convolutional neural networks (CNNs) and Transformer-based models. T...

A deep learning approach: physics-informed neural networks for solving a nonlinear telegraph equation with different boundary conditions.

The nonlinear Telegraph equation appears in a variety of engineering and science problems. This paper presents a deep learning algorithm termed physic...

Heterogeneous boundary synchronization of time-delayed competitive neural networks with adaptive learning parameter in the space-time discretized frames.

This article presents the master-slave time-delayed competitive neural networks in space-time discretized frames(STD-CNNs) with the heterogeneous stru...

Vertical federated learning based on data subset representation for healthcare application.

BACKGROUND AND OBJECTIVE: Artificial intelligence is increasingly essential for disease classification and clinical diagnosis tasks in healthcare. Giv...

QMaxViT-Unet+: A query-based MaxViT-Unet with edge enhancement for scribble-supervised segmentation of medical images.

The deployment of advanced deep learning models for medical image segmentation is often constrained by the requirement for extensively annotated datas...

Aligning large language models with radiologists by reinforcement learning from AI feedback for chest CT reports.

BACKGROUND: Large language models (LLMs) often struggle to fully capture the nuanced preferences and clinical judgement of radiologists in medical rep...

Practical X-ray gastric cancer diagnostic support using refined stochastic data augmentation and hard boundary box training.

Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it must be performed by a physician, which limits the n...