PoCaPNet: A Novel Approach for Surgical Phase Recognition Using Speech and X-Ray Images
| Autor: | Demir, Kubilay Can, Weise, Tobias, May, Matthias, Schmid, Axel, Maier, Andreas, Yang, Seung Hee |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.21437/Interspeech.2023-753 |
| Popis: | Surgical phase recognition is a challenging and necessary task for the development of context-aware intelligent systems that can support medical personnel for better patient care and effective operating room management. In this paper, we present a surgical phase recognition framework that employs a Multi-Stage Temporal Convolution Network using speech and X-Ray images for the first time. We evaluate our proposed approach using our dataset that comprises 31 port-catheter placement operations and report 82.56 \% frame-wise accuracy with eight surgical phases. Additionally, we investigate the design choices in the temporal model and solutions for the class-imbalance problem. Our experiments demonstrate that speech and X-Ray data can be effectively utilized for surgical phase recognition, providing a foundation for the development of speech assistants in operating rooms of the future. Comment: 5 Pages, 3 figures, INTERSPEECH 2023 |
| Databáze: | arXiv |
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