Deep convolutional neural networks for mammography: advances, challenges and applications
D Abdelhafiz, C Yang, R Ammar, S Nabavi - BMC bioinformatics, 2019 - Springer
Background The limitations of traditional computer-aided detection (CAD) systems for
mammography, the extreme importance of early detection of breast cancer and the high …
mammography, the extreme importance of early detection of breast cancer and the high …
[HTML][HTML] Deep learning to find colorectal polyps in colonoscopy: A systematic literature review
Colorectal cancer has a great incidence rate worldwide, but its early detection significantly
increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and …
increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and …
Exploiting the potential of unlabeled endoscopic video data with self-supervised learning
Purpose Surgical data science is a new research field that aims to observe all aspects of the
patient treatment process in order to provide the right assistance at the right time. Due to the …
patient treatment process in order to provide the right assistance at the right time. Due to the …
OFF-eNET: An optimally fused fully end-to-end network for automatic dense volumetric 3D intracranial blood vessels segmentation
Intracranial blood vessels segmentation from computed tomography angiography (CTA)
volumes is a promising biomarker for diagnosis and therapeutic treatment in …
volumes is a promising biomarker for diagnosis and therapeutic treatment in …
Deep feature learning for soft tissue sarcoma classification in MR images via transfer learning
Medical image analysis is motivated by deep learning emergence and computation power
increase. Meanwhile, relevant deep features can significantly enhance learnable expert and …
increase. Meanwhile, relevant deep features can significantly enhance learnable expert and …
A multi-sequences MRI deep framework study applied to glioma classfication
Glioma is one of the most important central nervous system tumors, ranked 15th in the most
common cancer for men and women. Magnetic Resonance Imaging (MRI) represents a …
common cancer for men and women. Magnetic Resonance Imaging (MRI) represents a …
Learning transformations for automated classification of manifestation of tuberculosis using convolutional neural network
A Abbas, MM Abdelsamea - 2018 13th International …, 2018 - ieeexplore.ieee.org
Automated classification of tuberculosis in x-ray images is of an increasing interest to all
researchers and physicians. Due to the high level of intensity inhomogeneity and variations …
researchers and physicians. Due to the high level of intensity inhomogeneity and variations …
Driver fatigue detection with single EEG channel using transfer learning
WM Shalash - 2019 IEEE International Conference on Imaging …, 2019 - ieeexplore.ieee.org
Decreasing road accidents rate and increasing road safety have been the major concerns
for a long time as traffic accidents expose the divers, passengers, properties to danger …
for a long time as traffic accidents expose the divers, passengers, properties to danger …
Classification of gastrointestinal images based on transfer learning and denoising convolutional neural networks
A Ahmed - Proceedings of International Conference on Data …, 2022 - Springer
Computer-aided diagnosis systems have become a common approach in helping doctors to
diagnose and recognise various diseases more quickly. Medical image classification which …
diagnose and recognise various diseases more quickly. Medical image classification which …
A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica
S Calderon-Ramirez, D Murillo-Hernandez… - Medical & biological …, 2022 - Springer
The implementation of deep learning-based computer-aided diagnosis systems for the
classification of mammogram images can help in improving the accuracy, reliability, and cost …
classification of mammogram images can help in improving the accuracy, reliability, and cost …