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.