Generating post-hoc explanation from deep neural networks for multi-modal medical image analysis tasks.

ABSTRACT: Explaining model decisions from medical image inputs is necessary for deploying deep neural network (DNN) based models as clinical decision assistants. The acquisition of multi-modal medical images is pervasive in practice for supporting the clinical decision-making process. Multi-modal images capture different aspects of the same underlying regions of interest. Explaining DNN decisions on multi-modal medical images is thus a clinically important problem. Our methods adopt commonly-used post-hoc artificial intelligence feature attribution methods to explain DNN decisions on multi-modal medical images, including two categories of gradient- and perturbation-based methods. • Gradient-based explanation methods - such as Guided BackProp, DeepLift - utilize the gradient signal to estimate the fe

SUBMITTER: Jin W 

PROVIDER: S-EPMC9922805 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature