A5103166161- Image Retrieval and Classification Techniques
- Advanced Image and Video Retrieval Techniques
- Multimodal Machine Learning Applications
- Biomedical Text Mining and Ontologies
- AI in cancer detection
- Topic Modeling
- Radiomics and Machine Learning in Medical Imaging
- Visual Attention and Saliency Detection
- Natural Language Processing Techniques
- Brain Tumor Detection and Classification
- Image and Video Quality Assessment
- Medical Image Segmentation Techniques
- Functional Brain Connectivity Studies
- Medical Imaging and Analysis
- Human Pose and Action Recognition
- COVID-19 diagnosis using AI
- Machine Learning in Healthcare
- Tuberculosis Research and Epidemiology
- EEG and Brain-Computer Interfaces
- Semantic Web and Ontologies
- Text and Document Classification Technologies
- Speech and dialogue systems
- Medical Imaging Techniques and Applications
- Coral and Marine Ecosystems Studies
- Schizophrenia research and treatment
CEA LIST
2024
University of Essex
2017-2024
National University of Distance Education
2024
Commissariat à l'Énergie Atomique et aux Énergies Alternatives
2024
Ludwig-Maximilians-Universität München
2023
Prince of Songkla University
2022
Southampton Solent University
2022
Roche (Spain)
2022
General Department of Preventive Medicine
2022
Universitatea Națională de Știință și Tehnologie Politehnica București
2020
Abstract In recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging (MRI) data due their potential discern subtle and intricate patterns. Despite the high performances reported in numerous studies, developing CNN models with good generalization abilities is still a challenging task possible leakage introduced during cross-validation (CV). this study, we quantitatively assessed effect of caused by 3D...
Alzheimer's Disease (AD) is a widespread neurodegenerative disease caused by structural changes in the brain and leads to deterioration of cognitive functions. Patients usually experience diagnostic symptoms at later stages after irreversible neural damage occurs. Therefore, early detection AD crucial start treatments decelerate progress maximize patients' quality life. With rapid advances machine learning scanning, may be possible via computer-assisted systems using neuroimaging data. Among...
Over the past decade, machine learning gained considerable attention from scientific community and has progressed rapidly as a result. Given its ability to detect subtle complicated patterns, deep (DL) been utilized widely in neuroimaging studies for medical data analysis automated diagnostics with varying degrees of success. In this paper, we question remarkable accuracies best performing models by assessing generalization performance state-of-the-art convolutional neural network (CNN) on...
According to a recent Deloitte study, the COVID-19 pandemic continues place huge strain on global health care sector. Covid-19 has also catalysed digital transformation across sector for improving operational efficiencies. As result, amount of digitally stored patient data such as discharge letters, scan images, test results or free text entries by doctors grown significantly. In 2020, 2314 exabytes medical was generated globally. This does not conform generic structure and is mostly in form...
Rapid and accurate diagnosis of Alzheimer's disease (AD) is critical for patient treatment, especially in the early stages disease. While computer-assisted based on neuroimaging holds vast potential helping clinicians detect sooner, there are still some technical hurdles to overcome. This study presents an end-to-end detection approach using convolutional autoencoders by integrating supervised prediction unsupervised representation. The 2D neural network upon a pre-trained autoencoder...