Special issues

Abstract

In past years Physics in Medicine and Biology (PMB) published some special 'conference issues' given over to papers from a specific event. This policy changed some years ago for several reasons. Firstly, IOP Publishing launched its own conference publication journal (the open access Journal of Physics: Conference Series). Secondly, producing special issues of PMB tended to adversely affect the scheduling of regular peer-reviewed papers. Thirdly, the growing number of requests to accommodate such special issues (presumably because of the popularity of the journal) led to an unworkable situation where worthy conferences had to be turned down. Fourthly, there was some concern about the uniformity of refereeing standards when conference organizers themselves refereed papers.However, it may not be so well known that the journal is very happy to consider grouping a number of research papers from a specific conference, subject to certain conditions of standardization. The papers must be taken through the usual (rigorous) refereeing process managed in-house by PMB. The conference organizer would be responsible for pre-filtering the papers so only the cream are submitted; they are also responsible for managing the close timing of submissions. In this issue we feature three of the best papers from ICMLA '08.The 2008 International Conference on Machine Learning and Applications (ICMLA '08) was held in San Diego, California, USA on 11–13 December 2008. The aim of the conference was to bring researchers working in the areas of machine learning and applications together. The conference covered both theoretical and experimental research results on machine learning applications in fields like medicine, biology, industry, virtual environments, game playing and problem solving. As part of the conference, a special session on 'Applications of Machine Learning in Radiotherapy' was organized by Dr Steve Jiang of University of California San Diego and Dr Martin Murphy of Virginia Commonwealth University. The aim was to provide a platform to present and discuss recent advancements in the application of machine learning methods in radiotherapy, specifically in helping accurately localize tumours in fluoroscopic images, precisely target the radiation to the tumours, analyze treatment outcomes, and improve treatment quality and patient safety.The journal welcomes papers from similar high-quality conferences and would organize to take them through this route.Steve Webb Editor-in-Chief Simon Harris Publisher