Lang DM, Peeken JC, Combs SE, Wilkens JJ, Bartzsch S (2022)
Publication Type: Conference contribution
Publication year: 2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
Conference Proceedings Title: ISBIC 2022 - International Symposium on Biomedical Imaging Challenges, Proceedings
Event location: Kolkata, IND
ISBN: 9781665451727
DOI: 10.1109/ISBIC56247.2022.9854698
The variety of treatment options for clinically localized renal masses is diverse. Medical imaging depicts a non-invasive technique able to retrieve detailed information that can be utilized for treatment decision. For the ISBI KNIGHT challenge, we studied the ability of deep neural networks for renal mass risk score prediction based on CT imaging and clinical features. A U-Net archi-tecture was trained for segmentation of the region of interest. Afterwards, patches of both kidneys were input into the convolutional layers of a neural network, resulting feature vectors were fused using the element-wise maximum and clinical features were merged with deep features for risk score classification. The model achieved an area under the receiver operating characteristics curve (AUC) of 0.814 on the test set for dis-crimination of clinical relevant subclasses. For the more de-tailed risk score prediction task a mean AUC of 0.676 was achieved.
APA:
Lang, D.M., Peeken, J.C., Combs, S.E., Wilkens, J.J., & Bartzsch, S. (2022). Risk Score Classification of Renal Masses on CT Imaging Data Using a Convolutional Neural Network. In ISBIC 2022 - International Symposium on Biomedical Imaging Challenges, Proceedings. Kolkata, IND: Institute of Electrical and Electronics Engineers Inc..
MLA:
Lang, Daniel M., et al. "Risk Score Classification of Renal Masses on CT Imaging Data Using a Convolutional Neural Network." Proceedings of the 2022 IEEE International Symposium on Biomedical Imaging Challenges, ISBIC 2022, Kolkata, IND Institute of Electrical and Electronics Engineers Inc., 2022.
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