Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images
P Afshar, S Heidarian, F Naderkhani… - Pattern Recognition …, 2020 - Elsevier
Abstract Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the
world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely …
world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely …
[HTML][HTML] Radiomics in breast imaging from techniques to clinical applications: a review
Recent advances in computer technology have generated a new area of research known as
radiomics. Radiomics is defined as the high throughput extraction and analysis of …
radiomics. Radiomics is defined as the high throughput extraction and analysis of …
Bayescap: A bayesian approach to brain tumor classification using capsule networks
P Afshar, A Mohammadi… - IEEE Signal Processing …, 2020 - ieeexplore.ieee.org
Convolutional neural networks (CNNs), which have been the state-of-the-art in many image-
related applications, are prone to losing important spatial information between image …
related applications, are prone to losing important spatial information between image …
Deep feature–based automatic classification of mammograms
Breast cancer has the second highest frequency of death rate among women worldwide.
Early-stage prevention becomes complex due to reasons unknown. However, some typical …
Early-stage prevention becomes complex due to reasons unknown. However, some typical …
[HTML][HTML] Deep learning-based segmentation of breast masses in dedicated breast CT imaging: Radiomic feature stability between radiologists and artificial intelligence
A deep learning (DL) network for 2D-based breast mass segmentation in unenhanced
dedicated breast CT images was developed and validated, and its robustness in radiomic …
dedicated breast CT images was developed and validated, and its robustness in radiomic …
Deep learning radiomics in breast cancer with different modalities: Overview and future
Recent improvements in deep learning radiomics (DLR) extracting high-level features form
medical imaging could promote the performance of computer aided diagnosis (CAD) for …
medical imaging could promote the performance of computer aided diagnosis (CAD) for …
[HTML][HTML] Deep learning-based radiomics of b-mode ultrasonography and shear-wave elastography: Improved performance in breast mass classification
X Zhang, M Liang, Z Yang, C Zheng, J Wu, B Ou… - Frontiers in …, 2020 - frontiersin.org
Objective Shear-wave elastography (SWE) can improve the diagnostic specificity of the B-
model ultrasonography (US) in breast cancer. However, whether deep learning-based …
model ultrasonography (US) in breast cancer. However, whether deep learning-based …
[HTML][HTML] 3D-MCN: a 3D multi-scale capsule network for lung nodule malignancy prediction
Despite the advances in automatic lung cancer malignancy prediction, achieving high
accuracy remains challenging. Existing solutions are mostly based on Convolutional Neural …
accuracy remains challenging. Existing solutions are mostly based on Convolutional Neural …
Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer
Radiomic features achieve promising results in cancer diagnosis, treatment response
prediction, and survival prediction. Our goal is to compare the handcrafted (explicitly …
prediction, and survival prediction. Our goal is to compare the handcrafted (explicitly …
[HTML][HTML] : deep learning-based radiomics for the time-to-event outcome prediction in lung cancer
Hand-crafted radiomics has been used for developing models in order to predict time-to-
event clinical outcomes in patients with lung cancer. Hand-crafted features, however, are pre …
event clinical outcomes in patients with lung cancer. Hand-crafted features, however, are pre …