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Biomedical image analysis

The biomedical image analysis laboratory is engaged in the development, optimization, and evaluation of bioengineering methods for processing bioimages. 

Specifically, research activities focus on the quantitative analysis of tomographic images for the extraction of biomarkers for the development of classification and prediction models in subjects affected by diseases. 

To achieve this, Artificial Intelligence techniques such as Machine Learning and Deep Learning are utilized and optimized, in combination with radiomic analysis of images. The execution of research activities involves collaboration with clinical and academic entities.


Research activities are focused on the characterization of pathological tissues (e.g. tumors, renal diseases) and healthy tissues affected by treatments, through radiomic analysis. This involves extracting a large number of quantitative information from biomedical images to estimate spatial patterns through texture analysis. Methods for image registration, pre-processing, and segmentation are developed and optimized to support radiomic analysis.
Research activities are focused on the development of Artificial Intelligence methods for the segmentation and characterization of tomographic images. In particular, approaches capable of quantifying the degree of uncertainty and confidence of Deep Learning methods are explored.


Elisa Scalco