AI applications to medical images: From machine learning to deep learning
Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …
research and healthcare services. This review focuses on challenges points to be clarified …
A review of methods for imbalanced multi-label classification
Abstract Multi-Label Classification (MLC) is an extension of the standard single-label
classification where each data instance is associated with several labels simultaneously …
classification where each data instance is associated with several labels simultaneously …
Towards understanding ensemble, knowledge distillation and self-distillation in deep learning
We formally study how ensemble of deep learning models can improve test accuracy, and
how the superior performance of ensemble can be distilled into a single model using …
how the superior performance of ensemble can be distilled into a single model using …
Software defect prediction using ensemble learning: A systematic literature review
Recent advances in the domain of software defect prediction (SDP) include the integration of
multiple classification techniques to create an ensemble or hybrid approach. This technique …
multiple classification techniques to create an ensemble or hybrid approach. This technique …
[HTML][HTML] COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios
Abstract Background and Objective: The COVID-19 can cause severe pneumonia and is
estimated to have a high impact on the healthcare system. Early diagnosis is crucial for …
estimated to have a high impact on the healthcare system. Early diagnosis is crucial for …
[HTML][HTML] Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank
UK Biobank is a large-scale prospective epidemiological study with all data accessible to
researchers worldwide. It is currently in the process of bringing back 100,000 of the original …
researchers worldwide. It is currently in the process of bringing back 100,000 of the original …
Emotion recognition using deep learning approach from audio–visual emotional big data
This paper proposes an emotion recognition system using a deep learning approach from
emotional Big Data. The Big Data comprises of speech and video. In the proposed system, a …
emotional Big Data. The Big Data comprises of speech and video. In the proposed system, a …
Ensembles of multiple models and architectures for robust brain tumour segmentation
Deep learning approaches such as convolutional neural nets have consistently
outperformed previous methods on challenging tasks such as dense, semantic …
outperformed previous methods on challenging tasks such as dense, semantic …
End-to-end environmental sound classification using a 1D convolutional neural network
In this paper, we present an end-to-end approach for environmental sound classification
based on a 1D Convolution Neural Network (CNN) that learns a representation directly from …
based on a 1D Convolution Neural Network (CNN) that learns a representation directly from …
Deep recurrent neural networks for human activity recognition
Adopting deep learning methods for human activity recognition has been effective in
extracting discriminative features from raw input sequences acquired from body-worn …
extracting discriminative features from raw input sequences acquired from body-worn …