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Found 14 papers, 4 papers with code
Modeling Power Systems Dynamics with Symbolic Physics-Informed Neural Networks
In recent years, scientific machine learning, particularly physic-informed neural networks (PINNs), has introduced new innovative methods to understanding the differential equations that describe power system dynamics, providing a more efficient alternative to traditional methods.
Toward Reinforcement Learning-based Rectilinear Macro Placement Under Human Constraints
Macro placement is a critical phase in chip design, which becomes more intricate when involving general rectilinear macros and layout areas.
Vision and Language Navigation in the Real World via Online Visual Language Mapping
Directly transferring SOTA navigation policies trained in simulation to the real world is challenging due to the visual domain gap and the absence of prior knowledge about unseen environments.
Evaluating the impact of an explainable machine learning system on the interobserver agreement in chest radiograph interpretation
We conducted a prospective study to measure the clinical impact of an explainable machine learning system on interobserver agreement in chest radiograph interpretation.
Solving Differential-Algebraic Equations in Power System Dynamic Analysis with Quantum Computing
Power system dynamics are generally modeled by high dimensional nonlinear differential-algebraic equations (DAEs) given a large number of components forming the network.
A Quantum Neural Network Regression for Modeling Lithium-ion Battery Capacity Degradation
Given the high power density low discharge rate and decreasing cost rechargeable lithium-ion batteries LiBs have found a wide range of applications such as power grid level storage systems electric vehicles and mobile devices.
Analyze the Effects of COVID-19 on Energy Storage Systems: A Techno-Economic Approach
During the COVID-19 pandemic, the U. S. power sector witnessed remarkable electricity demand changes in many geographical regions.
An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph
For the localization task with 14 types of lesions, our free-response receiver operating characteristic (FROC) analysis showed that the VinDr-CXR achieved a sensitivity of 80. 2% at the rate of 1. 0 false-positive lesion identified per scan.
Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices
In particular, deep convolutional neural networks (D-CNNs) have been key players and were adopted by the medical imaging community to assist clinicians and medical experts in disease diagnosis and treatment.
VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography
Mammography, or breast X-ray, is the most widely used imaging modality to detect cancer and other breast diseases.
Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis
As a result, the labeled data may contain a variety of human biases with a high rate of disagreement among annotators, which significantly affect the performance of supervised machine learning algorithms.
Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks
Chest radiograph (CXR) interpretation in pediatric patients is error-prone and requires a high level of understanding of radiologic expertise.
VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs
It demonstrates an area under the receiver operating characteristic curve (AUROC) of 88. 61% (95% CI 87. 19%, 90. 02%) for the image-level classification task and a mean average precision (mAP@0. 5) of 33. 56% for the lesion-level localization task.
Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network
Brain tumor segmentation plays an essential role in medical image analysis.
