Scope

Biomedical imaging has emerged as a major technological platform for disease diagnosis, clinical research, drug development and other related domains due to being non-invasive and producing multi-dimensional data. An abundance of imaging data is collected, but this wealth of information has not been utilized to full extent. Therefore, there is a need for introduction of biomedical image analysis techniques that are accurate, reproducible and generalizable to large scale datasets. Identification of imaging patterns in an anatomical site or an organ at the macroscopic or microscopic scale may guide characterization of abnormalities and estimation of disease risk, analysis of large scale clinical datasets, and assessment of intervention therapy techniques.

This special track solicits original and good-quality papers for delineating, identifying and characterizing novel biomedical imaging patterns. These methods may aim for segmentation, identification and quantification of anatomies, characterization of their properties, and classification of disease. These approaches may be applied to radiological imaging such as CT, MRI and ultrasound, or imaging at the cellular scale such as microscopy and digital pathology techniques.

Topics

Topics of interest include, but are not limited, to the following areas:

Machine/Deep Learning for Computer-aided Diagnosis and Prognosis

Biomedical Image Segmentation and Registration

Radiomics, Immunotherapies, Digital Pathology

Cell Segmentation and Tracking