Weakly supervised segmentation models as explainable radiological classifiers for lung tumour detection on CT images.

ABSTRACT:

Purpose

Interpretability is essential for reliable convolutional neural network (CNN) image classifiers in radiological applications. We describe a weakly supervised segmentation model that learns to delineate the target object, trained with only image-level labels ("image contains object" or "image does not contain object"), presenting a different approach towards explainable object detectors for radiological imaging tasks.

Methods

A weakly supervised Unet architecture (WSUnet) was trained to learn lung tumour segmentation from image-level labelled data. WSUnet generates voxel probability maps with a Unet and then constructs an image-level prediction by global max-pooling, thereby facilitating image-level training. WSUnet's voxel-level predictions were compared to tradition

SUBMITTER: O'Shea R 

PROVIDER: S-EPMC10657919 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature