MLMI 2017 : 8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017)
Call For Papers
8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017)
In conjunction with MICCAI 2017
September 10, 2017 in Quebec City, Quebec, Canada
- Accepted papers will be invited to submit to a special issue of a leading journal with a high impact factor
- The papers of MLMI2016 have been published in a special issue of Pattern Recognition (Impact factor: 3.399)
- Accepted papers will be published in LNCS proceeding.
- MLMI 2017 Best Paper Award will be presented to the best overall scientific paper.
- NVIDIA will sponsor again for the MLMI 2017 Best Paper Award!
Important Dates -
Full Paper Submission: June 12 (23:59 PST)
Notification of Acceptance: July 9
Camera-ready Version: July 23
Conference Date: September 10, 2017
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 2017) is the eighth in a series of workshops on this topic in conjunction with MICCAI 2017. 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.
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 The technical program will consist of previously unpublished, contributed, and invited papers.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., deep learning, 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
Dr. Qian Wang (Shanghai Jiao Tong University)
Dr. Yinghuan Shi (Nanjing University)
Dr. Heung-Il Suk (Korea University)
Dr. Kenji Suzuki (Illinois Institute of Technology)