Weakly‐supervised learning‐based pathology detection and localization in 3D chest CT scans

Abstract

BackgroundRecent advancements in anomaly detection have paved the way for novel radiological reading assistance tools that support the identification of findings, aimed at saving time. The clinical adoption of such applications requires a low rate of false positives while maintaining high sensitivity.PurposeIn light of recent interest and development in multi pathology identification, we present a novel method, based on a recent contrastive self‐supervised approach, for multiple chest‐related abnormality identification including low lung density area (“LLDA”), consolidation (“CONS”), nodules (“NOD”) and interstitial pattern (“IP”). Our approach alerts radiologists about abnormal regions within a computed tomography (CT) scan by providing 3D localization.MethodsWe introduce a new method for the classification and localization of multiple chest pathologies in 3D Chest CT scans. Our goal is to distinguish four common chest‐related abnormalities: “LLDA”, “CONS”, “NOD”, “IP” and “NORMAL”. This method is based on a 3D patch‐based classifier with a Resnet backbone encoder pretrained leveraging recent contrastive self supervised approach and a fine‐tuned classification head. We leverage the SimCLR contrastive framework for pretraining on an unannotated dataset of randomly selected patches and we then fine‐tune it on a labeled dataset. During inference, this classifier generates probability maps for each abnormality across the CT volume, which are aggregated to produce a multi‐label patient‐level prediction. We compare different training strategies, including random initialization, ImageNet weight initialization, frozen SimCLR pretrained weights and fine‐tuned SimCLR pretrained weights. Each training strategy is evaluated on a validation set for hyperparameter selection and tested on a test set. Additionally, we explore the fine‐tuned SimCLR pretrained classifier for 3D pathology localization and conduct qualitative evaluation.ResultsValidated on 111 chest scans for hyperparameter selection and subsequently tested on 251 chest scans with multi‐abnormalities, our method achieves an AUROC of 0.931 (95% confidence interval [CI]: [0.9034, 0.9557], ‐value < 0.001) and 0.963 (95% CI: [0.952, 0.976], ‐value < 0.001) in the multi‐label and binary (i.e., normal versus abnormal) settings, respectively. Notably, our method surpasses the area under the receiver operating characteristic (AUROC) threshold of 0.9 for two abnormalities: IP (0.974) and LLDA (0.952), while achieving values of 0.853 and 0.791 for NOD and CONS, respectively. Furthermore, our results highlight the superiority of incorporating contrastive pretraining within the patch classifier, outperforming Imagenet pretraining weights and non‐pretrained counterparts with uninitialized weights (F1 score = 0.943, 0.792, and 0.677 respectively). Qualitatively, the method achieved a satisfactory 88.8% completeness rate in localization and maintained an 88.3% accuracy rate against false positives.ConclusionsThe proposed method integrates self‐supervised learning algorithms for pretraining, utilizes a patch‐based approach for 3D pathology localization and develops an aggregation method for multi‐label prediction at patient‐level. It shows promise in efficiently detecting and localizing multiple anomalies within a single scan.