Index
Unsupervised detection of small hyperreflective features in ultrahigh resolution optical coherence tomography
Optical Character Recognition on Technical Drawings using Deep Learning
Offline-to-Online Handwriting Translation using Cyclic Consistency
Emotion Recognition in Comic Scenes with Multimodal Classifiers
Alzheimer’s Disease and Depression: A Bias Analysis and Machine Learning Investigation
Alzheimer’s disease is one of the most common neurodegenerative disorders that greatly impact individual and societal levels. These patients not only suffer from dementia but also from depression which can lead to more decline in cognitive abilities. However, both AD and depression have some common symptoms that make the detection of depression in Alzheimer’s extremely challenging. But several studies have used subsets of the DementiaBank database and employed different audio embeddings to detect depressive AD patients. Nevertheless, such embeddings can be biased for non-clinical factors.
Controlled CBCT Projection Generation Using Conditional Score-Based Diffusion Models
Improving Breast Abnormality Analysis in Mammograms using CycleGAN
Thesis_DescriptionDeep learning for brain metastases growth prediction
Deep Learning Reconstruction for Accelerated Water-Fat Magnetic Resonance Imaging
Parallel imaging is used to reconstruct MR images from undersampled multi-channel k-space
data which enables accelerated MR imaging with a high image quality. Reconstruction
techniques aim to correct for artifacts associated with the undersampling. One widely used reconstruction
method is SENSE which uses coil sensitivity encoding. In SENSE, the image
in every channel is calculated as the product of a high-resolution image and a smooth coil
sensitivity map. The main goal of this thesis is to develop a deep learning image reconstruction based on SENSE to boost
MR Imaging and correct for aliasing in accelerated Water-Fat imaging.