- Accepted papers will be invited to submit to a special issue of Pattern Recognition with extension: Submission website is http://ees.elsevier.com/pr/default.asp. Please select “SI:MLMI” for “Article Type” during the submission procedure. For details, please refer to the CALL_FOR_PAPERS. Note that the submission deadline was extended to January 31, 2016.
- The Best Paper Award: The MLMI 2015 presented the Best Paper Award to Bernard Ng, Anna Milazzo, and Andre Altmann, in recognition of excellence of their paper entitled “Node-based Gaussian Graphical Model for Identifying Discriminative Brain Regions from Connectivity Graphs“. The rigorous selection process is summarized as follows: Papers with an average score greater than or equal to 4.0 (out of 5.0) given by three reviewers on the program committee in the double-blinded review process were selected as award candidates.
- Accepted papers have been published in LNCS proceeding.
- The papers of MLMI2014 have been published as a special issue in IEEE Journal of Biomedical and Health Informatics.
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 2015) is the sixth in a series of workshops on this topic in conjunction with MICCAI 2015. 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 will be invited to submit to a special issue of Pattern Recognition. The MLMI 2015 Best Paper Award will be presented to the best overall scientific paper.
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 The technical program will consist of previously unpublished, contributed, and invited papers.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 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