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Found 15 papers, 7 papers with code
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.
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.
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.
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.
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.
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.
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.
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.
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%.
