Riunione Settimanale ML_INFN

lunedì 4 dic 2023, 16:00 → 17:00 Europe/Rome

Meeting room: https://l.infn.it/mlinfn-room

Minute: https://l.infn.it/mlinfn-minutes

16:00 → 17:00 A novel explainable approach in radiomics pipeline for local recurrence prediction of lung cancer: a feasibility study exploiting high energy physics potential to evaluate the model

AI models are likely to become useful tools to support clinical decision-making especially in high mortality diseases such as lung cancer. However, the black-box nature of these models remains nowadays the main challenge to be addressed to employ AI in the clinic. This work describes the preliminary stages and results obtained by implementing a novel explainable approach in radiomics pipeline for local recurrence prediction of lung cancer and its technical validation relying on the high energy physics domain.

Relatore: Mariagrazia Monteleone (Politecnico di Milano)