Search Results for author:
Found 11 papers, 5 papers with code
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.
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.
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.
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.
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-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.
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.
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%.
