1st International Workshop on

Multiscale Multimodal Medical Imaging (MMMI 2019)

In conjunction with 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, October 13, 2019, Shenzhen, China


Proceeding of the workshop, as part of the MICCAI 2019 conference proceeding, has been published as Lecture Notes in Computer Science (LNCS) book series. The proceeding is now available online at Springer

Inivtation letter for MMMI'19 is now available for downloading here

Workshop schedule is now online. This year we have the pleasure of inviting Prof. Jinyi Qi from UC Davis joined us for his keynote speech on "Pushing the temporal resolution of dynamic PET using multiscale information and the EXPLORER total-body PET scanner".

We are offering multiple Best Paper Awards and Student Paper Awards, thanks to the support from our sponsors!
Because of this, submission deadline has been extended to August 7th.


In the field of medical imaging, use of more than one modality (i.e. multimodal ) or more than one scale on the same target has become a growing field as more advanced techniques and devices have become available. For example, simultaneous acquisition of Positron Emission Tomography (PET) and Computed Tomography (CT) has become a standard clinical practice for a number of applications. Another example is the increasing interest in clinical diagnosis that combines high-resolution, localized pathological images and radiological images which captures disease at more global scale. Various analyses using multimodal medical imaging and computer-aided detection systems have been developed, with the premise that additional modalities can encompass abundant information which is different and complementary to each other. While methods and tools on multiscale image analysis are not widely developed and used. Facing the growing amount of data available from multiscale multimodal medical imaging facilities and a variety new methods for the image analysis developed so far, this MICCAI workshop aims to move forward the state of the art in multiscale multimodal medical imaging, including both algorithm development, implementation of methodology, and experimental studies. The workshop also aims to facilitate more interactions between researchers in the field of medical image analysis and the field of machine learning, especially in data fusion and multi-source learning.


MMMI aim to tackle the important challenge of dealing with medical images acquired from multiscale and multimodal imaging devices, which has been increasingly applied in research studies and clinical practice. This workshop offers an opportunity to present novel techniques and insights of multiscale multimodal medical images analysis, as well as empirical studies involving the application of multiscale multimodal imaging for clinical use.


Topic of submissions to the workshop include, but not limited to:
  • Image segmentation techniques based on multiscale multimodal images
  • Novel techniques in multiscale multimodal image acquisition
  • Registration methods across multiscale multimodal images
  • Fusion of images from multiple resolutions and novel visualization methods
  • Spatial-temporal analysis using multiple modalities
  • Fusion of image sources with different fidelities: e.g. co-analysis of EEG and fMRI
  • Multiscale multimodal disease classification and prediction using supervised or unsupervised methods
  • Atlas-based methods on multiple imaging modalities
  • Cross-modality image generative methods: e.g. generation of synthetic images between CT and MR
  • Novel radiomics methods based on multiscale multimodal imaging
  • Shape analysis on images from multiple sources and/or multiple resolution
  • Graph methods in medical image analysis
  • Benchmark studies for multiscale multimodal image analysis: e.g. using electrophysiological signals for validation of fMRI data
  • Cooperating Organization