Collaborating Authors

Looking back and thinking forwards -- 15 years of cardiology and cardiovascular research


J.M.K. leads both clinical and research groups in the Department of Heart Rhythm Disorders at the Royal Melbourne Hospital and University of Melbourne, Melbourne, Australia. He has an international reputation as a leader in the field of atrial arrhythmia research and has authored 380 peer-reviewed publications. He serves on the editorial board of 12 international cardiology journals and is an associate editor of JACC Clinical Electrophysiology. He is the immediate past president of the Asia Pacific Heart Rhythm Society and served as scientific chair of the Cardiac Society of Australia and New Zealand for 6 years. S.L. is Professor in Biochemistry & Molecular Biology at the Faculty of Chemical & Pharmaceutical Sciences and Professor in Cell & Molecular Biology in the Faculty of Medicine, University of Chile in Santiago, Chile and adjunct professor in the Cardiology Division, University of Texas Southwestern Medical Center in Dallas, USA.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge Artificial Intelligence

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e. 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that undergone gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

[Association Affairs] 2016 AAAS Fellows approved by the AAAS Council


In October 2016, the Council of the American Association for the Advancement of Science (AAAS) elected 391 members as Fellows of AAAS. These individuals will be recognized for their contributions to science and technology at the Fellows Forum to be held on 18 February 2017 during the AAAS Annual Meeting in Boston, MA. Herman B. Zimmerman, National Science Foundation (retired)

European Artificial Intelligence Innovation Summit (exl)


The implementation, data privacy, and operational challenges facing life science and healthcare professionals dedicated to integrating AI into their organization vastly differ by therapeutic area and patient population, size of the organization, and the number of resources available to them. Cookie-cutter solutions cannot address the unique challenges faced by a company. It is critical that the education available to these professionals meets the varying needs of the industry.