Silvia Dias Almeida

Silvia Dias Almeida is a PhD student at the Division of Medical Image Computing at the DKFZ. She received her Bachelor's and Master's degree in Biomedical Engineering at the NOVA School of Science and Technology, Lisbon. In her Master's thesis, Silvia developed an atlas-based semi-automatic segmentation algorithm of Whole-Body DWI images in order to quantify the tumor burden of multiple myeloma patients, at the Champalimaud Foundation. From 2018 to 2019, Silvia was awarded a FCT fellowship, where she studied the associations between brain metabolism measured by PET and neuroendocrine activity, in metastatic breast cancer patients. Later on, she was funded by FCT to join Alba Diz-Muñoz's lab at EMBL, where she developed image processing pipelines for analyzing cell polarity and motility. Currently, she's studying new deep-learning methods for the classification and monitoring of chronic obstructive pulmonary disease, in collaboration with the Heidelberg University Hospital.

Projects:

• Computational analysis of subclinical comorbidities in clinical routine CT data (komorbidom)
• Characterisation and prediction of COPD as a comorbidity from computed tomography imaging

Interests:

• Applied AI for medical problems
• Anomaly detection
• Registration

Publications

ORCID