Understanding computation time : a critical discussion of time as a computational performance metric

Computation time is an important performance metric that scientists and software engineers use to determine whether an algorithm is capable of running within a reasonable time frame. We provide an accessible critical review of the factors that influence computation time, highlighting problems in its reporting in current research and the negative practical impact that this has on developers, recommending best practice for its measurement and reporting. Discussing how computers and coders measure time, a discrepancy is exposed between best practice in the primarily theoretical field of computational complexity, and the difficulty for non-specialists in applying such theoretical findings. We therefore recommend establishing a better reporting practice, highlighting future work needed to expose the effects of poor reporting. Freely shareable templates are provided to help developers and researchers report this information more accurately, helping others to build upon their work, and thereby reducing the needless global duplication of computational and human effort.PostprintPeer reviewe

Anonymising pathology data using generative adversarial networks

Anonymising medical data for use in machine learning is important to preserve patient privacy and, in many circumstances, is a requirement before data can be made available. One approach to anonymising image data is to train a generative model to produce data that is statistically similar to the input data and then use the output of the model for downstream tasks, such as image classification, instead of the original sensitive data. In digital pathology, it's not yet well understood how using generative models to anonymise histology slide data impacts the performance of downstream tasks. To begin addressing this, we present an evaluation of a histology image classifier trained using patches extracted from the Camelyon 16 dataset and compare it to a classifier trained on the same number of synthetic images generated with a Deep Convolutional Generative Adversarial Network (DCGAN), from the same data. When predicting the class of an image patch as either cancer or normal it's shown that the accuracy reduces from 0.78 for original alone to 0.59 for synthetic alone, and the recall is significantly reduced from 0.70 to 0.44 when training exclusively on the same amount of synthetic data. If retaining a similar accuracy is required for the downstream task, then either the original data must be used or an improved anonymisation strategy must be devised. We conclude that using this DCGAN to anonymise the dataset, degrades the accuracy of the classifier which implies that it has failed to capture the required variation in the original data to generalise and act as a sufficient anonymisation strategy.Publisher PDFPublisher PD

Generative deep learning in digital pathology workflows

Funding: Supported by the Sir James Mackenzie Institute for Early Diagnosis, University of St Andrews and Industrial Centre for Artificial Intelligence Research in Digital Diagnostics (grant number TS/S013121/1).Many modern histopathology laboratories are in the process of digitising their workflows. Once images of the tissue exist as digital data, it becomes feasible to research the augmentation or automation of clinical reporting and diagnosis. The application of modern computer vision techniques, based on Deep Learning, promise systems that can identify pathologies in slide images with a high degree of accuracy. Generative modelling is an approach to machine learning and deep learning that can be used to transform and generate data. It can be applied to a broad range of tasks within digital pathology including the removal of color and intensity artefacts, the adaption of images in one domain into those of another, and the generation of synthetic digital tissue samples. This review provides an introduction to the topic, considers these applications, and discusses some future directions for generative models within histopathology.PostprintPeer reviewe

Classification of hyper-scale multimodal imaging datasets

Algorithms that classify hyper-scale multi-modal datasets, comprising of millions of images, into constituent modality types can help researchers quickly retrieve and classify diagnostic imaging data, accelerating clinical outcomes. This research aims to demonstrate that a deep neural network that is trained on a hyper-scale dataset (4.5 million images) composed of heterogeneous multi-modal data can be used to obtain significant modality classification accuracy (96%). By combining 102 medical imaging datasets, a dataset of 4.5 million images was created. A ResNet-50, ResNet-18, and VGG16 were trained to classify these images by the imaging modality used to capture them (Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and X-ray) across many body locations. The classification accuracy of the models was then tested on unseen data. The best performing model achieved classification accuracy of 96% on unseen data, which is on-par, or exceeds the accuracy of more complex implementations using EfficientNets or Vision Transformers (ViTs). The model achieved a balanced accuracy of 86%. This research shows it is possible to train Deep Learning (DL) Convolutional Neural Networks (CNNs) with hyper-scale multimodal datasets, composed of millions of images. Such models can find use in real-world applications with volumes of image data in the hyper-scale range, such as medical imaging repositories, or national healthcare institutions. Further research can expand this classification capability to include 3D-scans.Publisher PDFPeer reviewe

Ethics and Acceptance of Smart Homes for Older Adults

Societal challenges associated with caring for the physical and mental health of the elderly worldwide have grown at an unprecedented pace, increasing demand for healthcare services and technologies [1]. Despite the development of several assistive systems tailored to older adults, the rate of adoption of health technologies is low [2, 3]. This review discusses the ethical and acceptability challenges resulting in low adoption of health technologies specifically focused on smart homes for the elderly. The findings have been structured in two categories: Ethical Considerations (Privacy, Social Support, Autonomy) and Technology Aspects (User Context, Usability, Training). The findings conclude that the elderly community is more likely to adopt assistive systems when four key criteria are met. The technology should: be personalized towards their needs, protect their dignity and independence, provide user control, and not be isolating. Finally, we recommend researchers and developers working on assistive systems to: (1) Provide interfaces via smart devices to control and configure the monitoring system with feedback for the user, (2) Include various sensors/devices to architect a smart home solution in a way that is easy to integrate in daily life and (3) Define policies about data ownership

Reproducibility of deep learning in digital pathology whole slide image analysis

Funding: This work is supported by the Industrial Centre for AI Research in digital Diagnostics (iCAIRD) which is funded by Innovate UK on behalf of UK Research and Innovation (UKRI) [project number: 104690], and in part by Chief Scientist Office, Scotland.For a method to be widely adopted in medical research or clinical practice, it needs to be reproducible so that clinicians and regulators can have confidence in its use. Machine learning and deep learning have a particular set of challenges around reproducibility. Small differences in the settings or the data used for training a model can lead to large differences in the outcomes of experiments. In this work, three top-performing algorithms from the Camelyon grand challenges are reproduced using only information presented in the associated papers and the results are then compared to those reported. Seemingly minor details were found to be critical to performance and yet their importance is difficult to appreciate until the actual reproduction is attempted. We observed that authors generally describe the key technical aspects of their models well but fail to maintain the same reporting standards when it comes to data preprocessing which is essential to reproducibility. As an important contribution of the present study and its findings, we introduce a reproducibility checklist that tabulates information that needs to be reported in histopathology ML-based work in order to make it reproducible.Publisher PDFPeer reviewe

Automated Remote Pulse Oximetry System (ARPOS)

Funding: This research is funded by the School of Computer Science and by St Leonard’s Postgraduate College Doctoral Scholarship, both at the University of St Andrews for Pireh Pirzada’s PhD. Early work was funded by the Digital Health & Care Innovation Centre (DHI).Current methods of measuring heart rate (HR) and oxygen levels (SPO2) require physical contact, are individualised, and for accurate oxygen levels may also require a blood test. No-touch or non-invasive technologies are not currently commercially available for use in healthcare settings. To date, there has been no assessment of a system that measures HR and SPO2 using commercial off-the-shelf camera technology that utilises R, G, B and IR data. Moreover, no formal remote photoplethysmography studies have been done in real life scenarios with participants at home with different demographic characteristics. This novel study addresses all these objectives by developing, optimising, and evaluating a system that measures the HR and SPO2 of 40 participants. HR and SPO2 are determined by measuring the frequencies from different wavelength band regions using FFT and radiometric measurements after pre-processing face regions of interest (forehead, lips, and cheeks) from Colour, IR and Depth data. Detrending, interpolating, hamming, and normalising the signal with FastICA produced the lowest RMSE of 7.8 for HR with the r-correlation value of 0.85 and RMSE 2.3 for SPO2. This novel system could be used in several critical care settings, including in care homes and in hospitals and prompt clinical intervention as required.Publisher PDFPeer reviewe

SpeCam: sensing surface color and material with the front-facing camera of mobile device

SpeCam is a lightweight surface color and material sensing approach for mobile devices which only uses the front-facing camera and the display as a multi-spectral light source. We leverage the natural use of mobile devices (placing it face-down) to detect the material underneath and therefore infer the location or placement of the device. SpeCam can then be used to support discreet micro-interactions to avoid the numerous distractions that users daily face with today's mobile devices. Our two-parts study shows that SpeCam can i) recognize colors in the HSB space with 10 degrees apart near the 3 dominant colors and 4 degrees otherwise and ii) 30 types of surface materials with 99% accuracy. These findings are further supported by a spectroscopy study. Finally, we suggest a series of applications based on simple mobile micro-interactions suitable for using the phone when placed face-down.Postprin

Smart Homes for elderly to promote their health and wellbeing

The percentage of UK society aged 65 or above is projected to increase to 20.7% by 2027. This increases health challenges, including various physical and mental health concerns which creates a demand for health care services and technologies. This highly affects the cost of health services which exerts pressure on the national health services as well as private health care providers. There is a need for smart home systems which would preserve independence without compromising on their safety and promote their quality of life. Despite the development and availability of several assistive technologies tailored to support the elderly population, the rate of adoption is still low [3,4]. We aim to model and design an unobtrusive intelligent environment solution which boosts the rate of adoption among the elderly population in order to promote their health and wellbeing.PreprintPreprintPeer reviewe

RadarCat : Radar Categorization for input & interaction

The research described here was supported by the University of St Andrews and the Scottish Informatics and Computer Science Alliance (SICSA).In RadarCat we present a small, versatile radar-based system for material and object classification which enables new forms of everyday proximate interaction with digital devices. We demonstrate that we can train and classify different types of materials and objects which we can then recognize in real time. Based on established research designs, we report on the results of three studies, first with 26 materials (including complex composite objects), next with 16 transparent materials (with different thickness and varying dyes) and finally 10 body parts from 6 participants. Both leave one-out and 10-fold cross-validation demonstrate that our approach of classification of radar signals using random forest classifier is robust and accurate. We further demonstrate four working examples including a physical object dictionary, painting and photo editing application, body shortcuts and automatic refill based on RadarCat. We conclude with a discussion of our results, limitations and outline future directions.Postprin