Analogy-based post-treatment of CNN image segmentations
| Autor: | Justine Duck, Romain Schaller, Frédéric Auber, Yann Chaussy, Julien Henriet, Jean Lieber, Emmanuel Nauer, Henri Prade |
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| Přispěvatelé: | Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), K team (Data Science, Knowledge, Reasoning and Engineering), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Service de chirurgie pédiatrique [CHU Besançon], Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon), Laboratoire de nanomédecine, imagerie, thérapeutique (LNIT), Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing
analogical extrapolation medical image segmentation convolutional neural networks case-based reasoning [INFO.INFO-IM]Computer Science [cs]/Medical Imaging kidney cancer [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET] [INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
| Zdroj: | Case-Based Reasoning Research and Development: 30th International Conference, ICCBR 2022, Nancy, France, September 12–15, 2022, Proceedings ; ISBN: 978-3-031-14922-1 30th International Conference on Case-Based Reasoning (ICCBR 2022) 30th International Conference on Case-Based Reasoning (ICCBR 2022), Sep 2022, Nancy, France. pp.318-332, ⟨10.1007/978-3-031-14923-8_21⟩ Case-Based Reasoning Research and Development ISBN: 9783031149221 |
| DOI: | 10.1007/978-3-031-14923-8_21⟩ |
| Popis: | International audience; Convolutional neural networks (CNNs) have proven to be efficient tools for image segmentation when a large number of segmented images are available. However, when the number of segmented images is not so large, the CNN segmentations are less accurate. It is the case for nephroblastoma (kidney cancer) in particular. When a new patient arrives, the expert can only manually segment a sample of scanned images since manual segmentation is a time-consuming process. As a consequence, the question of how to compute accurate segmentations using both the trained CNN and such a sample is raised. A CBR approach based on proportional analogy is proposed in this paper. For a source image segmented by the expert, let a be the CNN segmentation of this image, b be its expert segmentation and c be the CNN segmentation of a target image close to the source image. The proposed approach aims at solving the analogical equation "a is to b as c is to d" with unknown d: the solution d of this equation is proposed as a segmentation of the target image. This approach and some of its improvements are evaluated and show an accuracy increase of the segmentation with respect to the CNN segmentation. |
| Databáze: | OpenAIRE |
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