Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus

• URL: http://arxiv.org/abs/2303.17915v1
• Date: Fri, 31 Mar 2023 09:23:27 GMT
• Title: Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus
• Authors: Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker, Dirk Beyersdorff, Elina Petersen, Marvin Petersen, Bastian Cheng, Dennis Eggert, Christian Betz, Anna Sophie Hoffmann, Alexander Schlaefer
• Abstract summary: Paranasal anomalies can present with a wide range of morphological features. Current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. We investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary (MS) and MS with polyps or cysts.
• Score: 46.1292414445895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these anomalies, especially when working with limited datasets. Additionally, current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. These challenges necessitate the need for further research and development in this area. In this study, we investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary sinuses (MS) and MS with polyps or cysts. The task of accurately identifying the relevant MS volume within larger head and neck Magnetic Resonance Imaging (MRI) scans can be difficult, but we develop a straightforward strategy to tackle this challenge. Our end-to-end solution includes the use of a novel sampling technique that not only effectively localizes the relevant MS volume, but also increases the size of the training dataset and improves classification results. Additionally, we employ a multiple instance ensemble prediction method to further boost classification performance. Finally, we identify the optimal size of MS volumes to achieve the highest possible classification performance on our dataset. With our multiple instance ensemble prediction strategy and sampling strategy, our 3D CNNs achieve an F1 of 0.85 whereas without it, they achieve an F1 of 0.70. We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy alongside a novel ensembling strategy that proves to be beneficial for paranasal anomaly classification in the MS.

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