All accepted papers are invited to submit to Special Issue on Journal of Biomedical and Health Informatics (Submission Deadline: 15 March 2018).

The MLMI workshop was successfully held with high-quality papers and a large number of audience of more than 250 people. We look forward to seeing you again in Granada, Spain in 2018!

Best paper winner: Guodong Zeng, Xin Yang, Jing Li, Lequan Yu, Pheng-Ann Heng, and Guoyan Zheng, with the title “3D U-net with Multi-Level Deep Supervision: Fully automatic segmentation of Proximal Femur in 3D MR Images”, Congratulations!

Keynote talk by Professor Anant Madabhushi is available to download. Great thanks to Professor Anant Madabhushi!


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.

  • 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!

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 The technical program will consist of previously unpublished, contributed, and invited papers.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

The MLMI 2017 is one of many satellite workshops occurring in conjunction with MICCAI 2017.

Cooperating Organizations

Best Paper Award Sponsor