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The field of medical imaging is an essential aspect of the medical sciences, involving various forms of radiation to capture images of the internal tissues and organs of the body. These images provide vital information for clinical…
Vision-language models (VLMs) have achieved impressive results on single-view vision tasks, but lack the multi-view spatial reasoning capabilities essential for embodied AI systems to understand 3D environments and manipulate objects across…
Medical image interpretation is central to most clinical applications such as disease diagnosis, treatment planning, and prognostication. In clinical practice, radiologists examine medical images and manually compile their findings into…
Recent advancements in Computer Assisted Diagnosis have shown promising performance in medical imaging tasks, particularly in chest X-ray analysis. However, the interaction between these models and radiologists has been primarily limited to…
In recent years, AI systems in the medical domain have advanced significantly. However, despite outperforming humans, they are rarely used in practice since it is often not clear how they make their decisions. Optimal explanation and…
Interactive volume visualization using a mixed reality (MR) system helps provide users with an intuitive spatial perception of volumetric data. Due to sophisticated requirements of user interaction and vision when using MR head-mounted…
A large portion of today's world population suffer from vision impairments and wear prescription eyeglasses. However, eyeglasses causes additional bulk and discomfort when used with augmented and virtual reality headsets, thereby negatively…
For decades, manufacturers have attempted to reduce or eliminate the optical aberrations that appear on the progressive addition lens' surfaces during manufacturing. Besides every effort made, some of these distortions are inevitable given…
Machine learning-based approaches outperform competing methods in most disciplines relevant to diagnostic radiology. Interventional radiology, however, has not yet benefited substantially from the advent of deep learning, in particular…
Chest X-Ray imaging is one of the most common radiological tools for detection of various pathologies related to the chest area and lung function. In a clinical setting, automated assessment of chest radiographs has the potential of…
This dissertation advances the state of the art for AR/VR tracking systems by increasing the tracking frequency by orders of magnitude and proposes an efficient algorithm for the problem of edge-aware optimization. AR/VR is a natural way of…
The widespread use of chest X-rays (CXRs), coupled with a shortage of radiologists, has driven growing interest in automated CXR analysis and AI-assisted reporting. While existing vision-language models (VLMs) show promise in specific tasks…
Diagnostic errors in radiology often occur due to incomplete visual assessments by radiologists, despite their knowledge of predicting disease classes. This insufficiency is possibly linked to the absence of required training in search…
Selection is one of the fundamental user interactions in virtual reality (VR) and 3D user interaction, and raycasting has been one of the most popular object selection techniques in VR. However, the selection of small or distant objects…
Increasing demands on medical imaging departments are taking a toll on the radiologist's ability to deliver timely and accurate reports. Recent technological advances in artificial intelligence have demonstrated great potential for…
Mixed reality (MR) environments are bound to become ubiquitous as MR technology becomes lighter, higher resolution, more affordable, and overall becomes a seamless extension of our current work and living spaces. For research scientists and…
Extended reality (XR) technology has the incredible potential to revolutionize mental health treatment and support, bringing a whole new dimension to the field. Through the use of immersive virtual and augmented reality experiences,…
This paper explores training medical vision-language models (VLMs) -- where the visual and language inputs are embedded into a common space -- with a particular focus on scenarios where training data is limited, as is often the case in…
The global demand for radiologists is increasing rapidly due to a growing reliance on medical imaging services, while the supply of radiologists is not keeping pace. Advances in computer vision and image processing technologies present…
Lately a new approach to Extended Reality (XR), denoted as XR-RF, has been proposed which is realized by combining Radio Frequency (RF) Imaging and programmable wireless environments (PWEs). RF Imaging is a technique that aims to detect…