Autoencoding the Retrieval Relevance of Medical Images

Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a $n/p/n$ autoencoder ($p\!<\!n$). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.Comment: To appear in proceedings of The 5th International Conference on Image Processing Theory, Tools and Applications (IPTA'15), Nov 10-13, 2015, Orleans, Franc

Workshop on the EHCR

This deliverable provides a summary report of a workshop on Electronic Health Records that was organised and delivered as the main focus of Workpackage 16 of the Semantic Mining project. The workshop was held as day three of a three-day series of events held in Brussels in late November 2004, under the umbrella and with kind support of the EUROREC organisation. This report provides a brief summary of that event, and includes in Annex 1 the complete delegate pack as printed and issued to all persons attending the event, This delegate pack included printed copies of all slides and screenshots used throughout the day. The workshop was well attended, and in particular the organisers are pleased to report that some very productive discussions took place that will act as the stimulus for new threads of research collaboration between various Semantic Mining partners, under the work plan of Workpackage 26. The organisers are grateful for the support of the EUROREC organisation in facilitating the organisation of this workshop and for lending their support to it through their web site and a personal endorsement of the event

Information Technology and Computing Topics and Their Relevance to Medical Undergraduate and Graduate Program Curricula at RIT

Two healthcare domain related programs in which this author has curricular relationships are the undergraduate Diagnostic Ultrasound (DU), and the graduate Master of Science in Health Informatics (MSHI). He teaches one course in the former and is the program coordinator for the latter. The undergraduate course is titled, “Computers in Medicine”, and is a rough 50% combination of a first-semester computing hardware course taught to our IT undergrads and another 50% of material from a textbook covering all the ways in which computing has benefitted various healthcare domains like, surgery, pharmacy, imaging, dentistry, psychiatry, remote medicine and the like. The MSHI program is a 30 semester credit hour program offered in an online format with a capstone experience (no thesis required) that was designed for professionals expecting to retool themselves for continued employment in a healthcare setting. This paper will discuss the details of the DU course and the MSHI program, the kind of computing content covered in each, and the rationale for and program design input of each. In conclusion, the reader will be left with an understanding of the what, when, how and why computing topics are necessarily required by these curricula, our justification for such, and how we might use that information in the development of future healthcare-related computing courses and potential programs. Course definition and program outline documents will be attached as appendices to the paper

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

INSPIRAL: investigating portals for information resources and learning. Final project report

INSPIRAL's aims were to identify and analyse, from the perspective of the UK HE learner, the nontechnical, institutional and end-user issues with regard to linking VLEs and digital libraries, and to make recommendations for JISC strategic planning and investment. INSPIRAL's objectives -To identify key stakeholders with regard to the linkage of VLEs, MLEs and digital libraries -To identify key stakeholder forum points and dissemination routes -To identify the relevant issues, according to the stakeholders and to previous research, pertaining to the interaction (both possible and potential) between VLEs/MLEs and digital libraries -To critically analyse identified issues, based on stakeholder experience and practice; output of previous and current projects; and prior and current research -To report back to JISC and to the stakeholder communities, with results situated firmly within the context of JISC's strategic aims and objectives

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings