Should end-to-end deep learning replace handcrafted radiomics?
Irène Buvat, Joyita Dutta, Abhinav K. Jha, Eliot L. Siegel, Fereshteh Yousefirizi, Arman Rahmim, Tyler Bradshaw
Abstract
learning.Representation learning involves training a neural network to learn a parsimonious yet comprehensive representation of the useful information present in the images.This information can then be used as latent deep radiomic features to build models corresponding to different classification or prediction tasks.Unlike handcrafted features, not only the values, but the definitions of the features involved in deep radiomics themselves depend on the training data and model architecture used to train a classification or prediction model.While deep learning is gaining ground thanks to the increasing availability of open datasets and shared deep learning models (https://nmmitools.org,https://monai.io),one might ask whether researchers should focus efforts exclusively on deep learning rather than radiomics.This short paper examines the respective positions of handcrafted and end-to-end deep radiomics in relation to key factors to be considered when developing and implementing classification or outcome prediction models.It has been inspired by the content of a moderated debate held during the SNMMI 2023 meeting involving two advocates of handcrafted radiomics and two advocates of deep radiomics.