TY - JOUR
T1 - Weakly supervised segmentation models as explainable radiological classifiers for lung tumour detection on CT images
AU - O'Shea, Robert
AU - Manickavasagar, Thubeena
AU - Horst, Carolyn
AU - Hughes, Daniel
AU - Cusack, James
AU - Tsoka, Sophia
AU - Cook, Gary
AU - Goh, Vicky
N1 - Funding Information:
Authors acknowledge funding support from the UK Research & Innovation London Medical Imaging and Artificial Intelligence Centre; Wellcome/Engineering and Physical Sciences Research Council Centre for Medical Engineering at King’s College London [WT 203148/Z/16/Z]; National Institute for Health Research Biomedical Research Centre at Guy’s & St Thomas’ Hospitals and King’s College London; National Institute for Health Research Biomedical Research Centre at Guy’s & St Thomas’ Hospitals and King’s College London; Cancer Research UK National Cancer Imaging Translational Accelerator [C1519/A28682]. For the purpose of open access, authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.
Publisher Copyright:
© 2023, The Author(s).
PY - 2023/11/19
Y1 - 2023/11/19
N2 - 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 traditional model interpretation techniques (class activation mapping, integrated gradients and occlusion sensitivity) in CT data from three institutions (training/validation: n = 412; testing: n = 142). Methods were compared using voxel-level discrimination metrics and clinical value was assessed with a clinician preference survey on data from external institutions. Results: Despite the absence of voxel-level labels in training, WSUnet’s voxel-level predictions localised tumours precisely in both validation (precision: 0.77, 95% CI: [0.76–0.80]; dice: 0.43, 95% CI: [0.39–0.46]), and external testing (precision: 0.78, 95% CI: [0.76–0.81]; dice: 0.33, 95% CI: [0.32–0.35]). WSUnet’s voxel-level discrimination outperformed the best comparator in validation (area under precision recall curve (AUPR): 0.55, 95% CI: [0.49–0.56] vs. 0.23, 95% CI: [0.21–0.25]) and testing (AUPR: 0.40, 95% CI: [0.38–0.41] vs. 0.36, 95% CI: [0.34–0.37]). Clinicians preferred WSUnet predictions in most instances (clinician preference rate: 0.72 95% CI: [0.68–0.77]). Conclusion: Weakly supervised segmentation is a viable approach by which explainable object detection models may be developed for medical imaging. Critical relevance statement: WSUnet 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: [Figure not available: see fulltext.].
AB - 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 traditional model interpretation techniques (class activation mapping, integrated gradients and occlusion sensitivity) in CT data from three institutions (training/validation: n = 412; testing: n = 142). Methods were compared using voxel-level discrimination metrics and clinical value was assessed with a clinician preference survey on data from external institutions. Results: Despite the absence of voxel-level labels in training, WSUnet’s voxel-level predictions localised tumours precisely in both validation (precision: 0.77, 95% CI: [0.76–0.80]; dice: 0.43, 95% CI: [0.39–0.46]), and external testing (precision: 0.78, 95% CI: [0.76–0.81]; dice: 0.33, 95% CI: [0.32–0.35]). WSUnet’s voxel-level discrimination outperformed the best comparator in validation (area under precision recall curve (AUPR): 0.55, 95% CI: [0.49–0.56] vs. 0.23, 95% CI: [0.21–0.25]) and testing (AUPR: 0.40, 95% CI: [0.38–0.41] vs. 0.36, 95% CI: [0.34–0.37]). Clinicians preferred WSUnet predictions in most instances (clinician preference rate: 0.72 95% CI: [0.68–0.77]). Conclusion: Weakly supervised segmentation is a viable approach by which explainable object detection models may be developed for medical imaging. Critical relevance statement: WSUnet 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: [Figure not available: see fulltext.].
UR - http://www.scopus.com/inward/record.url?scp=85177082243&partnerID=8YFLogxK
U2 - 10.1186/s13244-023-01542-2
DO - 10.1186/s13244-023-01542-2
M3 - Article
SN - 1869-4101
VL - 14
JO - Insights into imaging
JF - Insights into imaging
IS - 1
M1 - 195
ER -