MLMI 2014 - International Workshop on Machine Learning in Medical Imaging

Machine learning plays an essential role in the medical imaging field, including computer-aided diagnosis, image segmentation, image registration, image fusion, image-guided therapy, image annotation and image database retrieval. Machine Learning in Medical Imaging (MLMI 2014) is the fifth in a series of workshops on this topic in conjunction with MICCAI 2014. This workshop focuses on major trends and challenges in this area, and it presents work aimed to identify new cutting-edge techniques and their use in medical imaging. Accepted papers will be published in LNCS proceeding and invited to submit to a special issue of IEEE Journal of Biomedical and Health Informatics (previously IEEE Transactions on Information Technology in Biomedicine).
Objective
Our goal is to help advance the scientific research within the broad field of machine learning in medical imaging. The technical program will consist of previously unpublished, contributed, and invited papers. We are looking for original, high-quality submissions on innovative research and development in the analysis of medical image data using machine learning techniques.
Topics
Topics of interests include but are not limited to machine learning methods (e.g., support vector machines, statistical methods, manifold-space-based methods, artificial neural networks, extreme learning machines) with their applications to the following areas:
Medical image analysis (e.g., pattern recognition, classification, segmentation, registration) of anatomical structures and lesions
Computer-aided detection/diagnosis (e.g., for lung cancer, prostate cancer, breast cancer, colon cancer, brain diseases, liver cancer, acute disease, chronic disease, osteoporosis)
Multi-modality fusion (e.g., MRI/PET, PET/CT, projection X-ray/CT, X-ray/ultrasound) for diagnosis, image analysis and image guided interventions
Image reconstruction (e.g., expectation maximization (EM) algorithm, statistical methods, iterative reconstruction) for medical imaging (e.g., CT, PET, MRI, X-ray)
Image retrieval (e.g., context-based retrieval, lesion similarity)
Cellular image analysis (e.g., genotype, phenotype, classification, identification, cell tracking)
Molecular/pathologic image analysis (e.g., PET, digital pathology)
Dynamic, functional, physiologic, and anatomic imaging