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BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis
In the biomedical domain, taxonomies organize the acquisition modalities of scientific images in hierarchical structures.
DASS Good: Explainable Data Mining of Spatial Cohort Data
Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk.
THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy
Although cancer patients survive years after oncologic therapy, they are plagued with long-lasting or permanent residual symptoms, whose severity, rate of development, and resolution after treatment vary largely between survivors.
Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology
Advances in data collection in radiation therapy have led to an abundance of opportunities for applying data mining and machine learning techniques to promote new data-driven insights.
