Intermediate Task Fine-Tuning in Cancer Classification
- Autores
- Gramática, Martín Nicolás; García, Mario Alejandro; Ricapito, Juan Pablo
- Año de publicación
- 2023
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Reducing the amount of annotated data required to train predictive models is one of the main challenges in applying artificial intelligence to histopathology. In this paper, we propose a method to enhance the performance of deep learning models trained with limited data in the field of digital pathology. The method relies on a two-stage transfer learning process, where an intermediate model serves as a bridge between a pre-trained model on ImageNet and the final cancer classification model. The intermediate model is fine-tuned with a dataset of over 4,000,000 images weakly labeled with clinical data extracted from the TCGA program. The model obtained through the proposed method significantly outperforms a model trained with a traditional transfer learning process.
Fil: Gramática, Martín Nicolás . Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación en Inteligencia Artificial; Argentina.
Fil: García, Mario Alejandro. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación en Inteligencia Artificial; Argentina.
Fil: Ricapito, Juan Pablo. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación en Inteligencia Artificial; Argentina.
Peer Reviewed - Fuente
- Journal of Computer Science & Technology, Volume 23, Number 2, October 2023
- Materia
-
Histopathology
Digital pathology - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- Attribution-NonCommercial-NoDerivs 2.5 Argentina
- Repositorio
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- Institución
- Universidad Tecnológica Nacional
- OAI Identificador
- oai:ria.utn.edu.ar:20.500.12272/15600
Ver los metadatos del registro completo
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Intermediate Task Fine-Tuning in Cancer ClassificationGramática, Martín NicolásGarcía, Mario AlejandroRicapito, Juan PabloHistopathologyDigital pathologyReducing the amount of annotated data required to train predictive models is one of the main challenges in applying artificial intelligence to histopathology. In this paper, we propose a method to enhance the performance of deep learning models trained with limited data in the field of digital pathology. The method relies on a two-stage transfer learning process, where an intermediate model serves as a bridge between a pre-trained model on ImageNet and the final cancer classification model. The intermediate model is fine-tuned with a dataset of over 4,000,000 images weakly labeled with clinical data extracted from the TCGA program. The model obtained through the proposed method significantly outperforms a model trained with a traditional transfer learning process.Fil: Gramática, Martín Nicolás . Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación en Inteligencia Artificial; Argentina.Fil: García, Mario Alejandro. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación en Inteligencia Artificial; Argentina.Fil: Ricapito, Juan Pablo. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación en Inteligencia Artificial; Argentina.Peer ReviewedUniversidad Tecnológica Nacional Regional Córdoba.2026-09-16T20:06:33Z2023-06-27info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfXI Jornadas de Cloud Computing, Big Data & Emerging Topics, La Plata, Argentina. 2013.https://hdl.handle.net/20.500.12272/15600Journal of Computer Science & Technology, Volume 23, Number 2, October 2023reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacionalenginfo:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivs 2.5 Argentinahttp://creativecommons.org/licenses/by-nc-nd/2.5/ar/Gramática, Martín Nicolás; García, Mario Alejandro; Ricapito, Juan Pablohttps://creativecommons.org/licenses/by-nc-nd/4.0/2026-09-24T12:48:47Zoai:ria.utn.edu.ar:20.500.12272/15600instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:48:48.137Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse |
| dc.title.none.fl_str_mv |
Intermediate Task Fine-Tuning in Cancer Classification |
| title |
Intermediate Task Fine-Tuning in Cancer Classification |
| spellingShingle |
Intermediate Task Fine-Tuning in Cancer Classification Gramática, Martín Nicolás Histopathology Digital pathology |
| title_short |
Intermediate Task Fine-Tuning in Cancer Classification |
| title_full |
Intermediate Task Fine-Tuning in Cancer Classification |
| title_fullStr |
Intermediate Task Fine-Tuning in Cancer Classification |
| title_full_unstemmed |
Intermediate Task Fine-Tuning in Cancer Classification |
| title_sort |
Intermediate Task Fine-Tuning in Cancer Classification |
| dc.creator.none.fl_str_mv |
Gramática, Martín Nicolás García, Mario Alejandro Ricapito, Juan Pablo |
| author |
Gramática, Martín Nicolás |
| author_facet |
Gramática, Martín Nicolás García, Mario Alejandro Ricapito, Juan Pablo |
| author_role |
author |
| author2 |
García, Mario Alejandro Ricapito, Juan Pablo |
| author2_role |
author author |
| dc.subject.none.fl_str_mv |
Histopathology Digital pathology |
| topic |
Histopathology Digital pathology |
| dc.description.none.fl_txt_mv |
Reducing the amount of annotated data required to train predictive models is one of the main challenges in applying artificial intelligence to histopathology. In this paper, we propose a method to enhance the performance of deep learning models trained with limited data in the field of digital pathology. The method relies on a two-stage transfer learning process, where an intermediate model serves as a bridge between a pre-trained model on ImageNet and the final cancer classification model. The intermediate model is fine-tuned with a dataset of over 4,000,000 images weakly labeled with clinical data extracted from the TCGA program. The model obtained through the proposed method significantly outperforms a model trained with a traditional transfer learning process. Fil: Gramática, Martín Nicolás . Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación en Inteligencia Artificial; Argentina. Fil: García, Mario Alejandro. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación en Inteligencia Artificial; Argentina. Fil: Ricapito, Juan Pablo. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación en Inteligencia Artificial; Argentina. Peer Reviewed |
| description |
Reducing the amount of annotated data required to train predictive models is one of the main challenges in applying artificial intelligence to histopathology. In this paper, we propose a method to enhance the performance of deep learning models trained with limited data in the field of digital pathology. The method relies on a two-stage transfer learning process, where an intermediate model serves as a bridge between a pre-trained model on ImageNet and the final cancer classification model. The intermediate model is fine-tuned with a dataset of over 4,000,000 images weakly labeled with clinical data extracted from the TCGA program. The model obtained through the proposed method significantly outperforms a model trained with a traditional transfer learning process. |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023-06-27 2026-09-16T20:06:33Z |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
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article |
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publishedVersion |
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XI Jornadas de Cloud Computing, Big Data & Emerging Topics, La Plata, Argentina. 2013. https://hdl.handle.net/20.500.12272/15600 |
| identifier_str_mv |
XI Jornadas de Cloud Computing, Big Data & Emerging Topics, La Plata, Argentina. 2013. |
| url |
https://hdl.handle.net/20.500.12272/15600 |
| dc.language.none.fl_str_mv |
eng |
| language |
eng |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivs 2.5 Argentina http://creativecommons.org/licenses/by-nc-nd/2.5/ar/ Gramática, Martín Nicolás; García, Mario Alejandro; Ricapito, Juan Pablo https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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openAccess |
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Attribution-NonCommercial-NoDerivs 2.5 Argentina http://creativecommons.org/licenses/by-nc-nd/2.5/ar/ Gramática, Martín Nicolás; García, Mario Alejandro; Ricapito, Juan Pablo https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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pdf application/pdf |
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Universidad Tecnológica Nacional Regional Córdoba. |
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Universidad Tecnológica Nacional Regional Córdoba. |
| dc.source.none.fl_str_mv |
Journal of Computer Science & Technology, Volume 23, Number 2, October 2023 reponame:Repositorio Institucional Abierto (UTN) instname:Universidad Tecnológica Nacional |
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Universidad Tecnológica Nacional |
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Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional |
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gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar |
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13.265058 |