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.
Monteleone, M., Gennai, S., Govoni, P., Paganelli, C. (2023). 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. In ICBRA '23: Proceedings of the 2023 10th International Conference on Bioinformatics Research and Applications (pp.184-187) [10.1145/3632047.3632074].
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
Govoni P.;
2023
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.