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Found 48 papers, 9 papers with code
KnFu: Effective Knowledge Fusion
Conventional FL, however, is susceptible to gradient inversion attacks, restrictively enforces a uniform architecture on local models, and suffers from model heterogeneity (model drift) due to non-IID local datasets.
FH-TabNet: Multi-Class Familial Hypercholesterolemia Detection via a Multi-Stage Tabular Deep Learning
Despite its significance, application of Deep Learning (DL) for FH detection is in its infancy, possibly, due to categorical nature of the underlying clinical data.
HistoSegCap: Capsules for Weakly-Supervised Semantic Segmentation of Histological Tissue Type in Whole Slide Images
Digital pathology involves converting physical tissue slides into high-resolution Whole Slide Images (WSIs), which pathologists analyze for disease-affected tissues.
FedD2S: Personalized Data-Free Federated Knowledge Distillation
This paper addresses the challenge of mitigating data heterogeneity among clients within a Federated Learning (FL) framework.
NYCTALE: Neuro-Evidence Transformer for Adaptive and Personalized Lung Nodule Invasiveness Prediction
Distinct from conventional Computed Tomography (CT)-based Deep Learning (DL) models, the NYCTALE performs predictions only when sufficient amount of evidence is accumulated.
CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks
Mobile Edge Caching (MEC) integrated with Deep Neural Networks (DNNs) is an innovative technology with significant potential for the future generation of wireless networks, resulting in a considerable reduction in users' latency.
Transformer-based Hand Gesture Recognition via High-Density EMG Signals: From Instantaneous Recognition to Fusion of Motor Unit Spike Trains
Additionally, the CT-HGR framework can perform instantaneous recognition using sEMG image spatially composed from HD-sEMG signals.
HYDRA-HGR: A Hybrid Transformer-based Architecture for Fusion of Macroscopic and Microscopic Neural Drive Information
At the same time, advancements in acquisition of High-Density sEMG signals (HD-sEMG) have resulted in a surge of significant interest on sEMG decomposition techniques to extract microscopic neural drive information.
Light-weighted CNN-Attention based architecture for Hand Gesture Recognition via ElectroMyography
Advancements in Biological Signal Processing (BSP) and Machine-Learning (ML) models have paved the path for development of novel immersive Human-Machine Interfaces (HMI).
Hybrid Indoor Localization via Reinforcement Learning-based Information Fusion
The paper is motivated by the importance of the Smart Cities (SC) concept for future management of global urbanization.
ViT-CAT: Parallel Vision Transformers with Cross Attention Fusion for Popularity Prediction in MEC Networks
Followed by a Cross Attention (CA) module as the Fusion Center (FC), the proposed ViT-CAT is capable of learning the mutual information between temporal and spatial correlations, as well, resulting in improving the classification accuracy, and decreasing the model's complexity about 8 times.
Spatio-Temporal Hybrid Fusion of CAE and SWIn Transformers for Lung Cancer Malignancy Prediction
The paper proposes a novel hybrid discovery Radiomics framework that simultaneously integrates temporal and spatial features extracted from non-thin chest Computed Tomography (CT) slices to predict Lung Adenocarcinoma (LUAC) malignancy with minimum expert involvement.
Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks
Most existing datadriven popularity prediction models, however, are not suitable for the coded/uncoded content placement frameworks.
JUNO: Jump-Start Reinforcement Learning-based Node Selection for UWB Indoor Localization
Performance of UWB-based localization systems, however, can significantly degrade because of Non Line of Sight (NLoS) connections between a mobile user and UWB beacons.
AKF-SR: Adaptive Kalman Filtering-based Successor Representation
Recent studies in neuroscience suggest that Successor Representation (SR)-based models provide adaptation to changes in the goal locations or reward function faster than model-free algorithms, together with lower computational cost compared to that of model-based algorithms.
TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography
In other words, we propose a hybrid framework based on the transformer architecture, which is a relatively new and revolutionizing deep learning model.
ViT-HGR: Vision Transformer-based Hand Gesture Recognition from High Density Surface EMG Signals
The proposed Vision Transformer-based Hand Gesture Recognition (ViT-HGR) framework can overcome the aforementioned training time problems and can accurately classify a large number of hand gestures from scratch without any need for data augmentation and/or transfer learning.
Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark
Lung cancer is one of the deadliest cancers, and in part its effective diagnosis and treatment depend on the accurate delineation of the tumor.
DF-SSmVEP: Dual Frequency Aggregated Steady-State Motion Visual Evoked Potential Design with Bifold Canonical Correlation Analysis
To elicit SSmVEP, we designed a novel and innovative dual frequency aggregated modulation paradigm, referred to as the Dual Frequency Aggregated steady-state motion Visual Evoked Potential (DF-SSmVEP), by concurrently integrating "Radial Zoom" and "Rotation" motions in a single target without increasing the trial length.
BP-Net: Cuff-less, Calibration-free, and Non-invasive Blood Pressure Estimation via a Generic Deep Convolutional Architecture
Objective: The paper focuses on development of robust and accurate processing solutions for continuous and cuff-less blood pressure (BP) monitoring.
Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation
The proposed MAK-TD/SR frameworks consider the continuous nature of the action-space that is associated with high dimensional multi-agent environments and exploit Kalman Temporal Difference (KTD) to address the parameter uncertainty.
TEDGE-Caching: Transformer-based Edge Caching Towards 6G Networks
As a consequence of the COVID-19 pandemic, the demand for telecommunication for remote learning/working and telemedicine has significantly increased.
Hand Gesture Recognition Using Temporal Convolutions and Attention Mechanism
Advances in biosignal signal processing and machine learning, in particular Deep Neural Networks (DNNs), have paved the way for the development of innovative Human-Machine Interfaces for decoding the human intent and controlling artificial limbs.
CAE-Transformer: Transformer-based Model to Predict Invasiveness of Lung Adenocarcinoma Subsolid Nodules from Non-thin Section 3D CT Scans
Lung cancer is the leading cause of mortality from cancer worldwide and has various histologic types, among which Lung Adenocarcinoma (LUAC) has recently been the most prevalent one.
Data Shapley Value for Handling Noisy Labels: An application in Screening COVID-19 Pneumonia from Chest CT Scans
The SV of a data point, however, is not unique and depends on the learning model, the evaluation metric, and other data points collaborating in the training game.
Q-Net: A Quantitative Susceptibility Mapping-based Deep Neural Network for Differential Diagnosis of Brain Iron Deposition in Hemochromatosis
Brain iron deposition, in particular deep gray matter nuclei, increases with advancing age.
TEMGNet: Deep Transformer-based Decoding of Upperlimb sEMG for Hand Gestures Recognition
We propose a novel Vision Transformer (ViT)-based neural network architecture (referred to as the TEMGNet) to classify and recognize upperlimb hand gestures from sEMG to be used for myocontrol of prostheses.
Robust Framework for COVID-19 Identification from a Multicenter Dataset of Chest CT Scans
We showed that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, the model performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters.
Online Dynamic Window (ODW) Assisted Two-stage LSTM Frameworks for Indoor Localization
The second framework is developed based on a Signal Processing Dynamic Windowing (SP-DW) approach to further reduce the required processing time of the two-stage LSTM-based model.
DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection
Although UWB technology can enhance the accuracy of indoor positioning due to the use of a wide-frequency spectrum, there are key challenges ahead for its efficient implementation.
TB-ICT: A Trustworthy Blockchain-Enabled System for Indoor COVID-19 Contact Tracing
Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased to prevent spread of this highly contagious virus and be prepared for the potential future ones.
COVID-Rate: An Automated Framework for Segmentation of COVID-19 Lesions from Chest CT Scans
In this context, first, the paper introduces an open access COVID-19 CT segmentation dataset containing 433 CT images from 82 patients that have been annotated by an expert radiologist.
Human-level COVID-19 Diagnosis from Low-dose CT Scans Using a Two-stage Time-distributed Capsule Network
The AI model achieves COVID-19 sensitivity of 89. 5% +\- 0. 11, CAP sensitivity of 95% +\- 0. 11, normal cases sensitivity (specificity) of 85. 7% +\- 0. 16, and accuracy of 90% +\- 0. 06.
COVID19-HPSMP: COVID-19 Adopted Hybrid and Parallel Deep Information Fusion Framework for Stock Price Movement Prediction
Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction (COVID19-HPSMP), innovative fusion strategies are used to combine scattered social media news related to COVID-19 with historical mark data.
Diagnosis/Prognosis of COVID-19 Images: Challenges, Opportunities, and Applications
This has resulted in a surge of interest to develop Radiomics models for analysis and interpretation of medical images.
FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography
This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their widespread applications in human-machine interfaces.
COVID-FACT: A Fully-Automated Capsule Network-based Framework for Identification of COVID-19 Cases from Chest CT scans
The newly discovered Corona virus Disease 2019 (COVID-19) has been globally spreading and causing hundreds of thousands of deaths around the world as of its first emergence in late 2019.
CT-CAPS: Feature Extraction-based Automated Framework for COVID-19 Disease Identification from Chest CT Scans using Capsule Networks
Capsule Networks, on the other hand, can capture spatial relations, require smaller datasets, and have considerably fewer parameters.
COVID-CT-MD: COVID-19 Computed Tomography (CT) Scan Dataset Applicable in Machine Learning and Deep Learning
Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries affecting millions and claiming almost 1 million lives, since its emergence in late 2019.
MIXCAPS: A Capsule Network-based Mixture of Experts for Lung Nodule Malignancy Prediction
In particular, lung cancer is among the most common and deadliest cancers with a low 5-year survival rate.
MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement Learning
As a result, the proposed MM-KTD framework can learn the optimal policy with significantly reduced number of samples as compared to its DNN-based counterparts.
COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images
Pre-training with a dataset of similar nature further improved accuracy to 98. 3% and specificity to 98. 6%.
Siamese Neural Networks for EEG-based Brain-computer Interfaces
In this work, a Siamese architecture, which is developed based on Convolutional Neural Networks (CNN) and provides a binary output on the similarity of two inputs, is combined with OVR and OVO techniques to scale up for multi-class problems.
XceptionTime: A Novel Deep Architecture based on Depthwise Separable Convolutions for Hand Gesture Classification
The proposed innovative XceptionTime is designed by integration of depthwise separable convolutions, adaptive average pooling, and a novel non-linear normalization technique.
Capsule Networks for Brain Tumor Classification based on MRI Images and Course Tumor Boundaries
According to official statistics, cancer is considered as the second leading cause of human fatalities.
From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities
Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals and the availability of extensive set of medical images through internal/external communication systems, have resulted in a recent surge of significant interest in "Radiomics".
Improved Explainability of Capsule Networks: Relevance Path by Agreement
Recent advancements in signal processing and machine learning domains have resulted in an extensive surge of interest in deep learning models due to their unprecedented performance and high accuracy for different and challenging problems of significant engineering importance.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Brain Tumor Type Classification via Capsule Networks
Brain tumor is considered as one of the deadliest and most common form of cancer both in children and in adults.
