Ensemble deep learning: A review
Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …
performance. Currently, deep learning architectures are showing better performance …
An analysis on ensemble learning optimized medical image classification with deep convolutional neural networks
Novel and high-performance medical image classification pipelines are heavily utilizing
ensemble learning strategies. The idea of ensemble learning is to assemble diverse models …
ensemble learning strategies. The idea of ensemble learning is to assemble diverse models …
Unsupervised domain adaptation to classify medical images using zero-bias convolutional auto-encoders and context-based feature augmentation
The accuracy and robustness of image classification with supervised deep learning are
dependent on the availability of large-scale labelled training data. In medical imaging, these …
dependent on the availability of large-scale labelled training data. In medical imaging, these …
A spatial guided self-supervised clustering network for medical image segmentation
The segmentation of medical images is a fundamental step in automated clinical decision
support systems. Existing medical image segmentation methods based on supervised deep …
support systems. Existing medical image segmentation methods based on supervised deep …
Retinal image classification by self-supervised fuzzy clustering network
Y Luo, J Pan, S Fan, Z Du, G Zhang - IEEE Access, 2020 - ieeexplore.ieee.org
Diabetic retinal image classification aims to conduct diabetic retinopathy automatically
diagnosing, which has achieved considerable improvement by deep learning models …
diagnosing, which has achieved considerable improvement by deep learning models …
Review on hybrid segmentation methods for identification of brain tumor in MRI
Modalities like MRI give information about organs and highlight diseases. Organ information
is visualized in intensities. The segmentation method plays an important role in the …
is visualized in intensities. The segmentation method plays an important role in the …
DeepMCAT: large-scale deep clustering for medical image categorization
In recent years, the research landscape of machine learning in medical imaging has
changed drastically from supervised to semi-, weakly-or unsupervised methods. This is …
changed drastically from supervised to semi-, weakly-or unsupervised methods. This is …
Medical image classification for disease prediction with the aid of deep learning approaches
M Pandiyarajan, J Thimmiaraja… - 2022 2nd …, 2022 - ieeexplore.ieee.org
Machine learning as well as deep learning algorithms is recently in huge demand in the field
of image classification. This research paper illustrates the relevance of these two prospects …
of image classification. This research paper illustrates the relevance of these two prospects …
Using machine learning techniques to predict the cost of repairing hard failures in underground fiber optics networks
O Nyarko-Boateng, AF Adekoya, BA Weyori - Journal of Big Data, 2020 - Springer
Fiber optics cable has been adopted by telecommunication companies worldwide as the
primary medium of transmission. The cable is steadily replacing long-haul microwave …
primary medium of transmission. The cable is steadily replacing long-haul microwave …
[HTML][HTML] VAESim: A probabilistic approach for self-supervised prototype discovery
In medical image datasets, discrete labels are often used to describe a continuous spectrum
of conditions, making unsupervised image stratification a challenging task. In this work, we …
of conditions, making unsupervised image stratification a challenging task. In this work, we …