Frisch, Yannik ; Fuchs, Moritz ; Sanner, Antoine ; Ucar, Felix Anton ; Frenzel, Marius ; Wasielica-Poslednik, Joana ; Gericke, Adrian ; Wagner, Felix Mathias ; Dratsch, Thomas ; Mukhopadhyay, Anirban (2023)
Synthesising Rare Cataract Surgery Samples with Guided Diffusion Models.
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Vancouver (8.10-12.10.2023)
doi: 10.1007/978-3-031-43996-4_34
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Cataract surgery is a frequently performed procedure that demands automation and advanced assistance systems. However, gathering and annotating data for training such systems is resource intensive. The publicly available data also comprises severe imbalances inherent to the surgical process. Motivated by this, we analyse cataract surgery video data for the worst-performing phases of a pre-trained downstream tool classifier. The analysis demonstrates that imbalances deteriorate the classifier’s performance on underrepresented cases. To address this challenge, we utilise a conditional generative model based on Denoising Diffusion Implicit Models (DDIM) and Classifier-Free Guidance (CFG). Our model can synthesise diverse, high-quality examples based on complex multi-class multi-label conditions, such as surgical phases and combinations of surgical tools. We affirm that the synthesised samples display tools that the classifier recognises. These samples are hard to differentiate from real images, even for clinical experts with more than five years of experience. Further, our synthetically extended data can improve the data sparsity problem for the downstream task of tool classification. The evaluations demonstrate that the model can generate valuable unseen examples, allowing the tool classifier to improve by up to 10% for rare cases. Overall, our approach can facilitate the development of automated assistance systems for cataract surgery by providing a reliable source of realistic synthetic data, which we make available for everyone.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Frisch, Yannik ; Fuchs, Moritz ; Sanner, Antoine ; Ucar, Felix Anton ; Frenzel, Marius ; Wasielica-Poslednik, Joana ; Gericke, Adrian ; Wagner, Felix Mathias ; Dratsch, Thomas ; Mukhopadhyay, Anirban |
| Art des Eintrags: | Bibliographie |
| Titel: | Synthesising Rare Cataract Surgery Samples with Guided Diffusion Models |
| Sprache: | Englisch |
| Publikationsjahr: | 2023 |
| Ort: | Cham |
| Verlag: | Springer |
| Buchtitel: | Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 |
| Band einer Reihe: | 14228 |
| Veranstaltungstitel: | International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) |
| Veranstaltungsort: | Vancouver |
| Veranstaltungsdatum: | 8.10-12.10.2023 |
| DOI: | 10.1007/978-3-031-43996-4_34 |
| URL / URN: | https://doi.org/10.1007/978-3-031-43996-4_34 |
| Kurzbeschreibung (Abstract): | Cataract surgery is a frequently performed procedure that demands automation and advanced assistance systems. However, gathering and annotating data for training such systems is resource intensive. The publicly available data also comprises severe imbalances inherent to the surgical process. Motivated by this, we analyse cataract surgery video data for the worst-performing phases of a pre-trained downstream tool classifier. The analysis demonstrates that imbalances deteriorate the classifier’s performance on underrepresented cases. To address this challenge, we utilise a conditional generative model based on Denoising Diffusion Implicit Models (DDIM) and Classifier-Free Guidance (CFG). Our model can synthesise diverse, high-quality examples based on complex multi-class multi-label conditions, such as surgical phases and combinations of surgical tools. We affirm that the synthesised samples display tools that the classifier recognises. These samples are hard to differentiate from real images, even for clinical experts with more than five years of experience. Further, our synthetically extended data can improve the data sparsity problem for the downstream task of tool classification. The evaluations demonstrate that the model can generate valuable unseen examples, allowing the tool classifier to improve by up to 10% for rare cases. Overall, our approach can facilitate the development of automated assistance systems for cataract surgery by providing a reliable source of realistic synthetic data, which we make available for everyone. |
| Freie Schlagworte: | Generative Models, Denoising Diffusion Models, Cataract Surgery |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 11 Jun 2024 08:42 |
| Letzte Änderung: | 11 Jun 2024 09:01 |
| PPN: | 519025032 |
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