A5081019507- Functional Brain Connectivity Studies
- Machine Learning in Healthcare
- Osteoarthritis Treatment and Mechanisms
- COVID-19 diagnosis using AI
- Domain Adaptation and Few-Shot Learning
- Lower Extremity Biomechanics and Pathologies
- Artificial Intelligence in Healthcare and Education
- AI in cancer detection
- Dementia and Cognitive Impairment Research
- Explainable Artificial Intelligence (XAI)
- Fetal and Pediatric Neurological Disorders
- Radiomics and Machine Learning in Medical Imaging
- Brain Tumor Detection and Classification
- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
- Anomaly Detection Techniques and Applications
- Total Knee Arthroplasty Outcomes
- Neonatal and fetal brain pathology
- Infrared Thermography in Medicine
University of Calgary
2023-2024
Ontario Brain Institute
2023-2024
Manipal University Jaipur
2020
Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train weights networks on large datasets as well fine tuning pre-trained small datasets. Due to COVID-19 dataset available, neural be for diagnosis coronavirus. However, these applied chest CT image is very limited till now. Hence, main aim this paper use deep architectures an automated tool detection and CT. A DenseNet201 based transfer (DTL) proposed classify patients COVID infected...
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes novel disease (COVID-19) outbreak in more than 200 countries around the world. early diagnosis of infected patients is needed to discontinue this outbreak. infection from radiography images fastest method. In paper, two different ensemble deep transfer learning models have been designed for COVID-19 utilizing chest X-rays. Both utilized pre-trained better performance. They are able differentiate COVID-19, viral...
Skull-stripping is an important first step when analyzing brain Magnetic Resonance Imaging (MRI) data. Deep learning-based supervised segmentation models, such as the U-net model, have shown promising results in automating this task. However, it comes to newborn MRI data, there are no publicly available datasets that come with manually annotated masks be used labels during training of these models. Manual MR images time-consuming, labor-intensive, and requires expertise. Furthermore, using a...
Simulation studies, such as finite element (FE) modeling, provide insight into knee joint mechanics without patient involvement. Generic FE models mimic the biomechanical behavior of tissue, but overlook variations in geometry, loading, and material properties a population. Conversely, subject-specific include these factors, resulting enhanced predictive precision, are laborious time intensive. The present study aimed to enhance modeling by incorporating semi-automated segmentation algorithm...
Brain aging is a regional phenomenon, facet that remains relatively under-explored within the realm of brain age prediction research using machine learning methods. Voxel-level predictions can provide localized estimates granular insights into processes. This essential to understand differences in trajectories healthy versus diseased subjects. In this work, deep learning-based multitask model proposed for voxel-level from T1-weighted magnetic resonance images. The outperforms models existing...
Deep learning models have achieved state-of-the-art results in estimating brain age, which is an important health biomarker, from magnetic resonance (MR) images. However, most of these only provide a global age prediction, and rely on techniques, such as saliency maps to interpret their results. These highlight regions the input image that were significant for model's predictions, but they are hard be interpreted, map values not directly comparable across different samples. In this work, we...
Brain aging is a regional phenomenon, facet that remains relatively under-explored within the realm of brain age prediction research using machine learning methods. Voxel-level predictions can provide localized estimates granular insights into processes. This essential to understand differences in trajectories healthy versus diseased subjects. In this work, deep learning-based multitask model proposed for voxel-level from T1-weighted magnetic resonance images. The outperforms models existing...
While utilizing machine learning models, one of the most crucial aspects is how bias and fairness affect model outcomes for diverse demographics. This becomes especially relevant in context medical imaging applications as these models are increasingly being used diagnosis treatment planning. In this paper, we study biases related to sex when developing a based on brain magnetic resonance images (MRI). We investigate effects by performing age prediction considering different experimental...
Simulation studies like finite element (FE) modeling provide insight into knee joint mechanics without patient experimentation. Generic FE models represent biomechanical behavior of the tissue by overlooking variations in geometry, loading, and material properties a population. On other hand, subject-specific include these specifics, resulting enhanced predictive precision. However, creating such is laborious time-intensive. The present study aimed to enhance incorporating semi-automated...