Vortrag: Event Recognition in Laparoscopic Gynecology Videos with Hybrid Trans...
Stammdaten
Titel: | Event Recognition in Laparoscopic Gynecology Videos with Hybrid Transformers |
Beschreibung: | Analyzing laparoscopic surgery videos presents a complex and multifaceted challenge, with applications including surgical training, intra-operative surgical complication prediction, and post-operative surgical assessment. Identifying crucial events within these videos is a significant prerequisite in a majority of these applications. In this paper, we introduce a comprehensive dataset tailored for relevant event recognition in laparoscopic gynecology videos. Our dataset includes annotations for critical events associated with major intra-operative challenges and post-operative complications. To validate the precision of our annotations, we assess event recognition performance using several CNN-RNN architectures. Furthermore, we introduce and evaluate a hybrid transformer architecture coupled with a customized training-inference framework to recognize four specific events in laparoscopic surgery videos. Leveraging the Transformer networks, our proposed architecture harnesses inter-frame dependencies to counteract the adverse effects of relevant content occlusion, motion blur, and surgical scene variation, thus significantly enhancing event recognition accuracy. Moreover, we present a frame sampling strategy designed to manage variations in surgical scenes and the surgeons’ skill level, resulting in event recognition with high temporal resolution. We empirically demonstrate the superiority of our proposed methodology in event recognition compared to conventional CNN-RNN architectures through a series of extensive experiments. |
Schlagworte: | Medical Video Analysis, Laparoscopic Gynecology, Transformers |
Typ: | Angemeldeter Vortrag |
Homepage: | https://mmm2024.org/index.html |
Veranstaltung: | 30th International Conference on Multimedia Modeling (MMM 2024) (Amsterdam) |
Datum: | 01.02.2024 |
Vortragsstatus: | stattgefunden (Präsenz) |
Beteiligte
Sahar Nasirihaghighi (intern) |
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Negin Ghamsarian (extern) |
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Heinrich Husslein
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Klaus Schöffmann (intern) |
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Zuordnung
Organisation | Adresse | ||||
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
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Vortragsfokus |
Klassifikationsraster der zugeordneten Organisationseinheiten:
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TeilnehmerInnenkreis |
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Publiziert? |
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Kooperationen
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Unversity of Bern / Center for AI in Medicine
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CH - 3012 Bern |
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Medical University Vienna / Department of Gynecology and Gynecological Oncology
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AT - 1090 Wien |
Forschungsaktivitäten
(Achtung: Externe Aktivitäten werden im Suchergebnis nicht mitangezeigt)
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Vorträge | Keine verknüpften Vorträge vorhanden |