CC
C. Charlesworth
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
2 records found
1
This thesis investigates differences in microglia morphology between cognitively healthy centenarians and Alzheimer’s disease (AD) patients to better understand mechanisms of cognitive resilience and neurodegeneration. An end-to-end machine learning pipeline was developed for automated microglia detection, segmentation, morphological feature extraction, clustering, and statistical analysis. Key innovations include cross-species transfer learning, morphology-based active learning, topology-aware segmentation, morphology-centric evaluation metrics, and soft clustering of microglial states. By analyzing over one million cells from postmortem brain tissue, the study aims to identify morphology patterns associated with AD pathology and exceptional longevity, providing new insights into the role of microglia in neurodegeneration and healthy cognitive aging.
...
This thesis investigates differences in microglia morphology between cognitively healthy centenarians and Alzheimer’s disease (AD) patients to better understand mechanisms of cognitive resilience and neurodegeneration. An end-to-end machine learning pipeline was developed for automated microglia detection, segmentation, morphological feature extraction, clustering, and statistical analysis. Key innovations include cross-species transfer learning, morphology-based active learning, topology-aware segmentation, morphology-centric evaluation metrics, and soft clustering of microglial states. By analyzing over one million cells from postmortem brain tissue, the study aims to identify morphology patterns associated with AD pathology and exceptional longevity, providing new insights into the role of microglia in neurodegeneration and healthy cognitive aging.
Automatic Dysarthria Severity Assessment using Whisper-extracted Features
Evaluating ML architectures for dysarthria severity assessment on TORGO and MSDM
Dysarthria is a speech disorder commonly caused by neurological disorders such as strokes, cerebral palsy and Amyotrophic Lateral Sclerosis (ALS). The severity level of dysarthria greatly influences the appropriate treatment for a patient. However, assessing the severity of dysarthria in a patient is a time-consuming process that requires a trained speech therapist. Therefore the following work explores a variety of classifier architectures for automatic dysarthria severity assessment using Whisper encodings. The datasets used were MSDM and TORGO while the classifier architectures implemented included a Convolutional Neural Networks and Recurrent Neural Network variants. Across both datasets, the Gated Recurrent Unit network (GRU) achieved the best performance with 97.21% accuracy on MSDM and 97.47% on TORGO.
...
Dysarthria is a speech disorder commonly caused by neurological disorders such as strokes, cerebral palsy and Amyotrophic Lateral Sclerosis (ALS). The severity level of dysarthria greatly influences the appropriate treatment for a patient. However, assessing the severity of dysarthria in a patient is a time-consuming process that requires a trained speech therapist. Therefore the following work explores a variety of classifier architectures for automatic dysarthria severity assessment using Whisper encodings. The datasets used were MSDM and TORGO while the classifier architectures implemented included a Convolutional Neural Networks and Recurrent Neural Network variants. Across both datasets, the Gated Recurrent Unit network (GRU) achieved the best performance with 97.21% accuracy on MSDM and 97.47% on TORGO.