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To investigate the impact of OOD radiographs on existing chest X-rayclassification models and to increase their robustness against OOD data. Thestudy employed the commonly used chest X-ray classification model, CheXnet,trained on the chest X-ray 14 data set, and tested its robustness against OODdata using three public radiography data sets: IRMA, Bone Age, and MURA, andthe ImageNet data set. To detect OOD data for multi-label classification, weproposed in-distribution voting (IDV). The OOD detection performance ismeasured across data sets using the area under the receiver operatingcharacteristic curve (AUC) analysis and compared with Mahalanobis-based OODdetection, MaxLogit, MaxEnergy and self-supervised OOD detection (SS OOD).Without additional OOD detection, the chest X-ray classifier failed to discardany OOD images, with an AUC of 0.5. The proposed IDV approach trained on ID(chest X-ray 14) and OOD data (IRMA and ImageNet) achieved, on average, 0.999OOD AUC across the three data sets, surpassing all other OOD detection methods.Mahalanobis-based OOD detection achieved an average OOD detection AUC of 0.982.IDV trained solely with a few thousand ImageNet images had an AUC 0.913, whichwas higher than MaxLogit (0.726), MaxEnergy (0.724), and SS OOD (0.476). Theperformance of all tested OOD detection methods did not translate well toradiography data sets, except Mahalanobis-based OOD detection and the proposedIDV method. Training solely on ID data led to incorrect classification of OODimages as ID, resulting in increased false positive rates. IDV substantiallyimproved the model's ID classification performance, even when trained with datathat will not occur in the intended use case or test set, without additionalinference overhead.

A knee cannot have lung disease: out-of-distribution detection with in-distribution voting using the medical example of chest X-ray classification