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In clinical practice, radiologists are reliant on the lesion size whendistinguishing metastatic from non-metastatic lesions. A prerequisite forlesion sizing is their detection, as it promotes the downstream assessment oftumor spread. However, lesions vary in their size and appearance in CT scans,and radiologists often miss small lesions during a busy clinical day. Toovercome these challenges, we propose the use of state-of-the-art detectionneural networks to flag suspicious lesions present in the NIH DeepLesiondataset for sizing. Additionally, we incorporate a bounding box fusiontechnique to minimize false positives (FP) and improve detection accuracy.Finally, to resemble clinical usage, we constructed an ensemble of the bestdetection models to localize lesions for sizing with a precision of 65.17% andsensitivity of 91.67% at 4 FP per image. Our results improve upon or maintainthe performance of current state-of-the-art methods for lesion detection inchallenging CT scans.

Universal Lesion Detection in CT Scans using Neural Network Ensembles