REDUCING THE BURDEN OF CANCER WITH ARTIFICIAL INTELLIGENCE: IMAGE-BASED MODELS FOR BREAST CANCER DETECTION, ADVANCED STAGE RISK, AND CROSS-MODALITY VISUALIZATION

Ph.D

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

A survey of deep learning models in medical therapeutic areas.

pre-print1407 K

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions

Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning. This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in `Medical Imaging with Deep Learning' in the year 2018. This article surveys the recent developments in this direction, and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying Pattern Recognition tasks, and further sub-categorize it following a taxonomy based on human anatomy. This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging. This enables us to single out `lack of appropriately annotated large-scale datasets' as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of Computer Vision, Pattern Recognition and Machine Learning etc.; where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future

Radiological perspective of the formation of pressure ulcers - a comparison of pressure and experience on two imaging surfaces

Introduction: Pressure ulcers are a high cost, high volume issue for health and medical care providers, affecting patients’ recovery and psychological wellbeing. The current research of pressure on support surfaces as a risk factor in the development of pressure ulcers is not relevant to the specialised, controlled environment of the radiological setting. Method: 38 healthy participants aged 19-51 were positioned supine on two different imaging surfaces (X-ray Table & Mattressed Table). Interface pressure data was acquired using the XSENSOR pressure mapping over a time of 2073 minutes, preceded by 6 minutes settling time to reduce measurement error. Qualitative data regarding participants’ opinion of pain and comfort was recorded using a questionnaire. Data analysis was performed using SPSS 22. Results: Data was collected from 30 participants aged 19 to 51 (mean 25.77, SD 7.72), BMI from 18.7 to 33.6 (mean 24.12, SD 3.29), for both imaging surfaces, following eight participant exclusions. Total average pressure, average pressure for jeopardy areas (head, sacrum & heels) and peak pressure for jeopardy areas were calculated as interface pressure in mmHg. Qualitative data showed that a significant difference (P<0.05) in experiences of pain and discomfort between the two surfaces. A significant difference is seen in average pressure between the two surfaces. Conclusion: Pain and comfort data also show a significant difference between the surfaces. All findings support the proposal for further investigation into the effects of radiological surfaces and overlays as a risk factor for the formation of pressure ulcers

Theory and practice of mixed models applied to medical research

This thesis examines in depth the properties of mixed models and considers their application in a variety of designs used in medical research. Mixed models are a broad class of models which allow variation in the data to be modelled at several levels and take into account correlations occurring between observations. They offer several potential advantages over the more conventional fixed effects approaches: more efficient estimates, effective handling of missing data and more appropriate inference. The different types of mixed model are placed into a unified format and the properties of various fitting methods, including likelihood-based methods, least squares methods and the Bayesian approach, are considered in detail. The practical implications of using mixed models are examined and the submitted material would appear to be the first to consider these in such depth. The particular features of applying mixed models to a range of designs are considered including repeated measures, crossover, multi-centre, meta analysis, cluster randomised, hierarchical, bioequivalence and several more ad hoc designs. Novel approaches are introduced for sample size estimation and for analysing crossover designs with multiple periods, bioequivalence studies and case-control studies. Comparisons of mixed models with fixed effects models, which have often previously been the conventional approach, are given particular attention. Models suitable for both normal and non-normal data are considered and examples involving original analyses are used to illustrate the properties described. The published material comprises two editions of a textbook and ten journal publications