Multimodal and interpretable deep learning algorithm to classify lung nodules
Develop a multimodal algorithm (3 sources: proteomics, clinical data and CT image analysis) to analyse risk of lung cancer in a high risk population with at least one thoracic CT basal image
Develop a multimodal algorithm to refine the characterization of the risk of malignancy of indeterminate risk nodules found during LDCT screening or as incidental pulmonary nodules
Additionally, develop interpretability techniques which will allow us to understand the relationship of the different components of the algorithm to the pulmonary nodule segmented on the LDCT, and to lung cancer. A Interpretable Artificial Intelligence expert group is involved in this project.
Dr. Luis Seijo University of Navarra. Spain
Dr. Diego Serrano University of Navarra. Spain
Dr. Lara Lloret IFCA/CSIC. Santander. Spain
Miriam Cobo MSc IFCA/CSIC. Santander. Spain
Dr. Mike Davies University of Liverpool.UK
Prof. John Field. University of Liverpool. UK
Dr. Gorka Bastarrika University of Navarra. Spain
Dr. Juan Pablo de Torres University of Navarra. Spain
Dr. María Rodríguez University of Navarra. Spain
Dr. Valerio Perna University of Navarra. Spain
Dr. Maria D Lozano University of Navarra. Spain
Dr. Allan Argueta University of Navarra. Spain
Dr. Alfonso Calvo University of Navarra. Spain
Dr. Karmele Valencia. Center of Applied Medcial Research CIMA. Spain
Ana Belen Alcaide. University of Navarra. Spain
Ana Ezponda Casajús. University of Navarra. Spain
Julián Sanz Ortega University of Navarra. Spain
Jennifer Barranco University of Navarra. Spain
Andrea Pasquier University of Navarra Spain
Maria del Mar Ocon. University of Navarra. Spain