Part-Prototype Models in Medical Imaging: Applications and Current Challenges
Recent developments in Artificial Intelligence have increasingly focused on explainability research. The potential of Explainable Artificial Intelligence (XAI) in producing trustworthy computer-aided diagnosis systems and its usage for knowledge discovery are gaining interest in the medical imaging...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | BioMedInformatics |
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| Online Access: | https://www.mdpi.com/2673-7426/4/4/115 |
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| author | Lisa Anita De Santi Franco Italo Piparo Filippo Bargagna Maria Filomena Santarelli Simona Celi Vincenzo Positano |
| author_facet | Lisa Anita De Santi Franco Italo Piparo Filippo Bargagna Maria Filomena Santarelli Simona Celi Vincenzo Positano |
| author_sort | Lisa Anita De Santi |
| collection | DOAJ |
| description | Recent developments in Artificial Intelligence have increasingly focused on explainability research. The potential of Explainable Artificial Intelligence (XAI) in producing trustworthy computer-aided diagnosis systems and its usage for knowledge discovery are gaining interest in the medical imaging (MI) community to support the diagnostic process and the discovery of image biomarkers. Most of the existing XAI applications in MI are focused on interpreting the predictions made using deep neural networks, typically including attribution techniques with saliency map approaches and other feature visualization methods. However, these are often criticized for providing incorrect and incomplete representations of the black-box models’ behaviour. This highlights the importance of proposing models intentionally designed to be self-explanatory. In particular, part-prototype (PP) models are interpretable-by-design computer vision (CV) models that base their decision process on learning and identifying representative prototypical parts from input images, and they are gaining increasing interest and results in MI applications. However, the medical field has unique characteristics that could benefit from more advanced implementations of these types of architectures. This narrative review summarizes existing PP networks, their application in MI analysis, and current challenges. |
| format | Article |
| id | doaj-art-a592cd0bb066421b90cb7a66f2d4618c |
| institution | Kabale University |
| issn | 2673-7426 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | BioMedInformatics |
| spelling | doaj-art-a592cd0bb066421b90cb7a66f2d4618c2024-12-27T14:13:18ZengMDPI AGBioMedInformatics2673-74262024-10-01442149217210.3390/biomedinformatics4040115Part-Prototype Models in Medical Imaging: Applications and Current ChallengesLisa Anita De Santi0Franco Italo Piparo1Filippo Bargagna2Maria Filomena Santarelli3Simona Celi4Vincenzo Positano5Department of Information Engineering, University of Pisa, 56122 Pisa, ItalyDepartment of Information Engineering, University of Pisa, 56122 Pisa, ItalyDepartment of Information Engineering, University of Pisa, 56122 Pisa, ItalyCNR Institute of Clinical Physiology, 56124 Pisa, ItalyBioengineering Unit, Fondazione Toscana G. Monasterio, 56124 Pisa, ItalyBioengineering Unit, Fondazione Toscana G. Monasterio, 56124 Pisa, ItalyRecent developments in Artificial Intelligence have increasingly focused on explainability research. The potential of Explainable Artificial Intelligence (XAI) in producing trustworthy computer-aided diagnosis systems and its usage for knowledge discovery are gaining interest in the medical imaging (MI) community to support the diagnostic process and the discovery of image biomarkers. Most of the existing XAI applications in MI are focused on interpreting the predictions made using deep neural networks, typically including attribution techniques with saliency map approaches and other feature visualization methods. However, these are often criticized for providing incorrect and incomplete representations of the black-box models’ behaviour. This highlights the importance of proposing models intentionally designed to be self-explanatory. In particular, part-prototype (PP) models are interpretable-by-design computer vision (CV) models that base their decision process on learning and identifying representative prototypical parts from input images, and they are gaining increasing interest and results in MI applications. However, the medical field has unique characteristics that could benefit from more advanced implementations of these types of architectures. This narrative review summarizes existing PP networks, their application in MI analysis, and current challenges.https://www.mdpi.com/2673-7426/4/4/115deep learningXAIinterpretability-by-designpart-prototype modelsmedical imaging |
| spellingShingle | Lisa Anita De Santi Franco Italo Piparo Filippo Bargagna Maria Filomena Santarelli Simona Celi Vincenzo Positano Part-Prototype Models in Medical Imaging: Applications and Current Challenges BioMedInformatics deep learning XAI interpretability-by-design part-prototype models medical imaging |
| title | Part-Prototype Models in Medical Imaging: Applications and Current Challenges |
| title_full | Part-Prototype Models in Medical Imaging: Applications and Current Challenges |
| title_fullStr | Part-Prototype Models in Medical Imaging: Applications and Current Challenges |
| title_full_unstemmed | Part-Prototype Models in Medical Imaging: Applications and Current Challenges |
| title_short | Part-Prototype Models in Medical Imaging: Applications and Current Challenges |
| title_sort | part prototype models in medical imaging applications and current challenges |
| topic | deep learning XAI interpretability-by-design part-prototype models medical imaging |
| url | https://www.mdpi.com/2673-7426/4/4/115 |
| work_keys_str_mv | AT lisaanitadesanti partprototypemodelsinmedicalimagingapplicationsandcurrentchallenges AT francoitalopiparo partprototypemodelsinmedicalimagingapplicationsandcurrentchallenges AT filippobargagna partprototypemodelsinmedicalimagingapplicationsandcurrentchallenges AT mariafilomenasantarelli partprototypemodelsinmedicalimagingapplicationsandcurrentchallenges AT simonaceli partprototypemodelsinmedicalimagingapplicationsandcurrentchallenges AT vincenzopositano partprototypemodelsinmedicalimagingapplicationsandcurrentchallenges |