ML-CDS 2022: Multimodal Learning and Fusion Across Scales for Clinical Decision Support


In conjunction with MICCAI 2022, Singapore,

This is the 12th workshop in the series on bringing clinicians and medical imaging researchers to discuss topics in clinical decision support and translation to practice of machine learning for medical imaging. Diagnostic decision-making (using images and other clinical data) is still very much an art for many physicians in their practices today due to a lack of quantitative tools and measurements. Recent wave of deep learning for medical imaging is showing new promise for building clinical decision support systems. However, their translation and adoption to clinical practice has been slow with high expectations on accuracies for such systems in terms of both precision and recall. Furthermore, with medical images being acquired at multiple scales and/or multiple from modalities, multimodal fusion techniques have been increasingly applied in research studies and clinical practice to integrate and make sense of the patient data across scales of observation.

The goal of this workshop is to bring together imaging researchers and clinicians tackling the important challenges of acquiring and interpreting multimodality data at multiple scales for clinical decision support and treatment planning, to present and discuss latest developments in the field. Specifically, researchers interested in multimodal fusion across scales, multimodal imaging and genomic data analysis, and machine learning/AI communities will be co-located with clinicians who have a use for computer-aided diagnosis and clinical decision support tools. We will discuss new techniques of multimodal learning and their translation to clinical decision support in practice. We are looking for original, high-quality submissions that address innovative research and development in the learning of multimodal medical data for use in clinical decision support and treatment planning. In addition, this year, we are adding a focus on fusing multimodal data covering topics of multi-modal image acquisition and reconstruction, novel methodologies and insights of multiscale multimodal medical images analysis, and empirical studies involving the application of multiscale multimodal imaging for clinical use.