A5081055281- Glaucoma and retinal disorders
- Retinal Imaging and Analysis
- Planarian Biology and Electrostimulation
- Plant and Biological Electrophysiology Studies
- Digital Imaging for Blood Diseases
- Optical Coherence Tomography Applications
- Explainable Artificial Intelligence (XAI)
- Adversarial Robustness in Machine Learning
- Machine Learning in Materials Science
- Biofield Effects and Biophysics
- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
- Dementia and Cognitive Impairment Research
- Fungal Biology and Applications
- Brain Tumor Detection and Classification
Universitat Oberta de Catalunya
2021-2025
University College London
2024
Early diagnosis of Alzheimer's disease and its prodromal stage, also known as mild cognitive impairment (MCI), is critical since some patients with progressive MCI will develop the disease. We propose a multi-stream deep convolutional neural network fed patch-based imaging data to classify stable MCI. First, we compare MRI images cognitively normal subjects identify distinct anatomical landmarks using multivariate statistical test. These are then used extract patches that into proposed...
Glaucoma, a leading cause of irreversible blindness worldwide, poses significant diagnostic challenges due to its reliance on subjective evaluation. Recent advances in computer vision and deep learning have demonstrated the potential for automated assessment. This paper provides comprehensive survey studies AI-based glaucoma diagnosis using fundus, optical coherence tomography, visual field images, with focus learning-based methods. We searched Web Science, PubMed, IEEE Xplore, Google...
Glaucoma, a leading cause of irreversible blindness, necessitates early detection for accurate and timely intervention to prevent vision loss. In this study, we present novel deep learning framework that leverages the diagnostic value 3D Optical Coherence Tomography (OCT) imaging automated glaucoma detection. framework, integrate pre-trained Vision Transformer on retinal data rich slice-wise feature extraction bidirectional Gated Recurrent Unit capturing inter-slice spatial dependencies....
Fungi cells can sense extracellular signals via reception, transduction, and response mechanisms, allowing them to communicate with their host adapt environment. They feature effective regulatory protein expressions that enhance regulate adaptation various triggers such as stress, hormones, physical stimuli light, factors. In our recent studies, we have shown Pleurotus oyster fungi generate electrical potential impulses in the form of spike events exposure environmental, mechanical, chemical...
Glaucoma, a leading cause of irreversible blindness, necessitates early detection for accurate and timely intervention to prevent vision loss. In this study, we present novel deep learning framework that leverages the diagnostic value 3D Optical Coherence Tomography (OCT) imaging automated glaucoma detection. framework, integrate pre-trained Vision Transformer on retinal data rich slice-wise feature extraction bidirectional Gated Recurrent Unit capturing inter-slice spatial dependencies....
To equip Convolutional Neural Networks (CNNs) with explainability, it is essential to interpret how opaque models take specific decisions, understand what causes the errors, improve architecture design, and identify unethical biases in classifiers. This paper introduces ADVISE, a new explainability method that quantifies leverages relevance of each unit feature map provide better visual explanations. this end, we propose using adaptive bandwidth kernel density estimation assign score respect...