Abstract
Labeling for pathology detection is a laborious task, performed by highly trained and expensive experts. Datasets often have mixed formats, including a mix of pathology positional labels and categorical labels. Successfully combining mixed-format data from multiple institutions for model training and evaluation is critical for model generalization. Herein, we describe a novel machine-learning method to augment a categorical dataset with positional information. This is inspired by the emerging data-centric AI paradigm, which focuses on systematically changing data to improve performance, rather than changing the model. In order to improve on a baseline of reducing the positional labels to categorical data, we propose a generalizable two-stage method that directs model attention to regions where pathologies are highly likely to occur, exploiting all the mixed-format data. The proposed approach was evaluated using four different knee MRI pathology detection tasks, including anterior cruciate ligament (ACL) integrity and injury age (5082 cases), and medial compartment cartilage (MCC) high-grade defects and subchondral edema detection (4251 cases). For these tasks, we achieved specificities and sensitivities between 90–94% and 78–93%, respectively, which were comparable to the inter-reader agreement results. On all tasks, we report an increase in AUC score, and an average of 8% specificity and 4% sensitivity improvement, as compared to the baseline approach. Combining a UNet network with a morphological peak-finding algorithm, our method also provides defect localization, with average accuracies between 4.3–5.1 mm. In addition, we demonstrate that our model generalizes well on a publicly available ACL tear dataset of 717 cases, without re-training, achieving 90% specificity and 100% sensitivity. The proposed method can be used to optimize image classification tasks in other medical or non-medical domains, which often have a mixture of categorical and positional labels.
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Kornreich, M. et al. (2022). Combining Mixed-Format Labels for AI-Based Pathology Detection Pipeline in a Large-Scale Knee MRI Study. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13438. Springer, Cham. https://doi.org/10.1007/978-3-031-16452-1_18
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DOI: https://doi.org/10.1007/978-3-031-16452-1_18
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