| [188468] |
| Title: Multiple instance ensembling for paranasal anomaly classification in the maxillary sinus. |
| Written by: D. Bhattacharya and F. Behrendt and B. T. Becker and D. Beyersdorff and E. Petersen and M. Petersen and B. Cheng and D. Eggert and C. Betz and A. S. Hoffmann and A. Schlaefer |
| in: <em>International Journal of Computer Assisted Radiology and Surgery</em>. (2023). |
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| DOI: 10.1007/s11548-023-02990-3 |
| URL: https://doi.org/10.1007/s11548-023-02990-3 |
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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.