The College of Engineering and Computer Science: Recent submissions
Now showing items 61-80 of 143
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Ultra‐broadband and fexible metamaterial absorber based on MoS2 cuboids with Mie resonances
(2023-03-02)In this work, we present a type of flexible and broadband metamaterial absorber operating in the GHz range. The proposed structure consists of three layers: a periodic square-shaped array made of molybdenum disulfide (MoS2) ... -
Unified Energy-Based Generative Network for Supervised Image Hashing
(2023)Hashing methods often face critical efficiency challenges, such as generalization with limited labeled data, and robustness issues (such as changes in the data distribution and missing information in the input data) in ... -
Use of a convolutional neural network and quantitative ultrasound for diagnosis of fatty liver
(2022-03-01)Quantitative ultrasound (QUS) was used to classify rabbits that were induced to have liver disease by placing them on a fatty diet for a defined duration and/or periodically injecting them with CCl4. The ground truth of ... -
VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations
(2022)Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and ... -
VinDr-Mammo: A large-scale benchmark dataset for computer- aided diagnosis in full-feld digital mammography
(2023)Mammography, or breast X-ray imaging, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assisted detection and diagnosis (CADe/x) ... -
Learning from multiple expert annotators for enhancing anomaly detection in medical image analysis
(2023-04-10)Cardiovascular diseases (CVDs) are a group of heart and blood vessel disorders that is one of the most serious dangers to human health, and the number of such patients is still growing. Early and accurate detection plays ... -
Learning from multiple expert annotators for enhancing anomaly detection in medical image analysis
(2022-03-20)Building an accurate computer-aided diagnosis system based on data-driven approaches requires a large amount of high-quality labeled data. In medical imaging analysis, multiple expert annotators often produce subjective ... -
XPGAN: X-ray projected generative adversarial network for improving COVID-19 image classification
(2021-04-13)This work aims to fight against the current outbreak pandemic by developing a method to classify suspected infected COVID-19 cases. Driven by the urgency, due to the vastly increased number of patients and deaths worldwide, ... -
ZeVis: A Visual Analytics System for Exploration of a Larval Zebrafish Brain in Serial-Section Electron Microscopy Images
(2021-05-26)The automation and improvement of nano-scale electron microscopy imaging technologies have expanded a push in neuroscience to understand brain circuits at the scale of individual cells and their connections. Most of this ... -
Improving transformers with probabilistic attention keys
(2022)Multi-head attention is a driving force behind state-of-the-art transformers, which achieve remarkable performance across a variety of natural language processing (NLP) and computer vision tasks. It has been observed that ... -
Improving Pareto front learning via multi-sample hypernetworks
(2023-04-28)Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping function from a given trade-off vector to a solution on the Pareto front, which solves the multi-objective optimization (MOO) ... -
Image-based contextual pill recognition with medical knowledge graph assistance
(2022)In many healthcare applications, accurately identifying pills from images captured under varying conditions has become increasingly crucial. Despite numerous attempts to employ deep learning methods for pill recognition, ... -
Hardness-guided domain adaptation to recognize biomedical named entities under low-resource scenarios
(2022)Domain adaptation is a promising approach to address data scarcity in low-resource scenarios. However, applying it to token-level tasks like biomedical Named Entity Recognition (bioNER) poses challenges due to the unique ... -
GOAL: Gist-set online active learning for efficient chest X-ray image annotation
(2021)Deep learning in medical image analysis often requires extensive high-quality labeled data to achieve human-level accuracy. We propose Gist-set Online Active Learning (GOAL), a novel solution to the challenge of limited ... -
Glucose sensors based on chitosan capped ZnS doped Mn nanomaterials
(2023-01-27)A typical glucose sensor is a glucose oxidase (GOx) enzyme-based sensor due to its high sensitivity and selectivity. However, it has difficulty activating and stabilizing the enzyme. To enhance the stability of GOx-based ... -
Flexible HTTP-based video adaptive streaming for good QoE during sudden bandwidth drops
(2023)We have observed a boom in video streaming over the Internet, especially during the Covid-19 pandemic, that could exceed the network resource availability. In addition to upgrading the network infrastructure, finding a way ... -
FedDRL: Deep reinforcement learning-based adaptive aggregation for non-IID data in federated learning
(2022-08-04)The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions ... -
Enhancing deep learning-based 3-lead ECG classification with heartbeat counting and demographic data integration
(2022-08-15)Nowadays, an increasing number of people are being diagnosed with cardiovascular diseases (CVDs), the leading cause of death globally. The gold standard for identifying these heart problems is via electrocardiogram (ECG). ... -
Efficient two-party integer comparison with block vectorization mechanism
(2021-09-13)Private integer comparison has been an essential computation function for many applications, including online auctions, credential identification, data mining, and joint bidding. In the setting of two-party computation, ... -
Does informativeness matter? Active learning for educational dialogue act classification
(2023-04-12)Dialogue Acts (DAs) can be used to explain what expert tutors do and what students know during the tutoring process. Most empirical studies adopt the random sampling method to obtain sentence samples for manual annotation ...
