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

ConclusionWeakly supervised segmentation is a viable approach by which explainable object detection models may be developed for medical imaging.Critical relevance statementWSUnet learns to segment images at voxel level, training only with image-level labels. A Unet backbone first generates a voxel-level probability map and then extracts the maximum voxel prediction as the image-level prediction. Thus, training uses only image-level annotations, reducing human workload. WSUnet ’s voxel-level predictions provide a causally verifiable explanation for its image-level prediction, improving interpretability.Key points• Explainability and interpretability are essential for reliable medical image classifiers.• This study applies weakly supervised segmentation to generate explainable image classifiers.• The weakly supervised Unet inherently explains its image-level predictions at voxel level.Graphical Abstract
Source: Insights into Imaging - Category: Radiology Source Type: research