Benjamin M. Gorman

M. Jorge Cardoso, Wenqi Li, Richard Brown et al.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

Garreth W. Tigwell, Zhanna Sarsenbayeva, Benjamin M. Gorman et al.

Situationally-induced impairments and disabilities (SIIDs) make it difficult for users of interactive computing systems to perform tasks due to context (e.g., listening to a phone call when in a noisy crowd) rather than a result of a congenital or acquired impairment (e.g., hearing damage). SIIDs are a great concern when considering the ubiquitousness of technology in a wide range of contexts. Considering our daily reliance on technology, and mobile technology in particular, it is increasingly important that we fully understand and model how SIIDs occur. Similarly, we must identify appropriate methods for sensing and adapting technology to reduce the effects of SIIDs. In this workshop, we will bring together researchers working on understanding, sensing, modelling, and adapting technologies to ameliorate the effects of SIIDs. This workshop will provide a venue to identify existing research gaps, new directions for future research, and opportunities for future collaboration.

Benjamin M. Gorman

At least 360 million people worldwide have disabling hearing loss that frequently causes difficulties in day-to-day conversations. Hearing aids often fail to offer enough benefits and have low adoption rates. However, people with hearing loss find that speechreading can improve their understanding during conversation. Speechreading (often called lipreading) refers to using visual information about the movements of a speaker's lips, teeth, and tongue to help understand what they are saying. Speechreading is commonly used by people with all severities of hearing loss to understand speech, and people with typical hearing also speechread (albeit subconsciously) to help them understand others. However, speechreading is a skill that takes considerable practice to acquire. Publicly-funded speechreading classes are sometimes provided, and have been shown to improve speechreading acquisition. However, classes are only provided in a handful of countries around the world and students can only practice effectively when attending class. Existing tools have been designed to help improve speechreading acquisition, but are often not effective because they have not been designed within the context of contemporary speechreading lessons or practice. To address this, in this thesis I present a novel speechreading acquisition framework that can be used to design Speechreading Acquisition Tools (SATs) - a new type of technology to improve speechreading acquisition.

Michael Mauderer, Garreth W. Tigwell, Benjamin M. Gorman et al.

We propose that accessibility research can lay the foundation for technology that can be used to augment the perception of everyone. To show how this can be achieved, we present three case studies of our research in which we demonstrate our approaches for impaired colour vision, situational visual impairments and situational hearing impairment.