How deeply to fine-tune a convolutional neural network
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2020 |
| Outros Autores: | |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10362/99426 |
Resumo: | Kandel, I., & Castelli, M. (2020). How deeply to fine-tune a convolutional neural network: A case study using a histopathology dataset. Applied Sciences (Switzerland), 10(10), [3359]. https://doi.org/10.3390/APP10103359 |
| id |
RCAP_77f29d8e123a7cbf151d7e4f0f22153b |
|---|---|
| oai_identifier_str |
oai:run.unl.pt:10362/99426 |
| network_acronym_str |
RCAP |
| network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| repository_id_str |
7160 |
| spelling |
How deeply to fine-tune a convolutional neural networkA case study using a histopathology datasetConvolutional neural networkDeep learningFine-tuningImage classificationMedical imagesTransfer learningMaterials Science(all)InstrumentationEngineering(all)Process Chemistry and TechnologyComputer Science ApplicationsFluid Flow and Transfer ProcessesKandel, I., & Castelli, M. (2020). How deeply to fine-tune a convolutional neural network: A case study using a histopathology dataset. Applied Sciences (Switzerland), 10(10), [3359]. https://doi.org/10.3390/APP10103359Accurate classification of medical images is of great importance for correct disease diagnosis. The automation of medical image classification is of great necessity because it can provide a second opinion or even a better classification in case of a shortage of experienced medical staff. Convolutional neural networks (CNN) were introduced to improve the image classification domain by eliminating the need to manually select which features to use to classify images. Training CNN from scratch requires very large annotated datasets that are scarce in the medical field. Transfer learning of CNN weights from another large non-medical dataset can help overcome the problem of medical image scarcity. Transfer learning consists of fine-tuning CNN layers to suit the new dataset. The main questions when using transfer learning are how deeply to fine-tune the network and what difference in generalization that will make. In this paper, all of the experiments were done on two histopathology datasets using three state-of-the-art architectures to systematically study the effect of block-wise fine-tuning of CNN. Results show that fine-tuning the entire network is not always the best option; especially for shallow networks, alternatively fine-tuning the top blocks can save both time and computational power and produce more robust classifiers.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNKandel, IbrahemCastelli, Mauro2020-06-15T22:52:23Z2020-05-122020-05-12T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article20application/pdfhttp://hdl.handle.net/10362/99426eng2076-3417PURE: 18599038https://doi.org/10.3390/APP10103359info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:46:21Zoai:run.unl.pt:10362/99426Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:39:10.772118Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| dc.title.none.fl_str_mv |
How deeply to fine-tune a convolutional neural network A case study using a histopathology dataset |
| title |
How deeply to fine-tune a convolutional neural network |
| spellingShingle |
How deeply to fine-tune a convolutional neural network Kandel, Ibrahem Convolutional neural network Deep learning Fine-tuning Image classification Medical images Transfer learning Materials Science(all) Instrumentation Engineering(all) Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
| title_short |
How deeply to fine-tune a convolutional neural network |
| title_full |
How deeply to fine-tune a convolutional neural network |
| title_fullStr |
How deeply to fine-tune a convolutional neural network |
| title_full_unstemmed |
How deeply to fine-tune a convolutional neural network |
| title_sort |
How deeply to fine-tune a convolutional neural network |
| author |
Kandel, Ibrahem |
| author_facet |
Kandel, Ibrahem Castelli, Mauro |
| author_role |
author |
| author2 |
Castelli, Mauro |
| author2_role |
author |
| dc.contributor.none.fl_str_mv |
NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School RUN |
| dc.contributor.author.fl_str_mv |
Kandel, Ibrahem Castelli, Mauro |
| dc.subject.por.fl_str_mv |
Convolutional neural network Deep learning Fine-tuning Image classification Medical images Transfer learning Materials Science(all) Instrumentation Engineering(all) Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
| topic |
Convolutional neural network Deep learning Fine-tuning Image classification Medical images Transfer learning Materials Science(all) Instrumentation Engineering(all) Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
| description |
Kandel, I., & Castelli, M. (2020). How deeply to fine-tune a convolutional neural network: A case study using a histopathology dataset. Applied Sciences (Switzerland), 10(10), [3359]. https://doi.org/10.3390/APP10103359 |
| publishDate |
2020 |
| dc.date.none.fl_str_mv |
2020-06-15T22:52:23Z 2020-05-12 2020-05-12T00:00:00Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/99426 |
| url |
http://hdl.handle.net/10362/99426 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
2076-3417 PURE: 18599038 https://doi.org/10.3390/APP10103359 |
| dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
20 application/pdf |
| dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
| instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
| instacron_str |
RCAAP |
| institution |
RCAAP |
| reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
| repository.mail.fl_str_mv |
|
| _version_ |
1799138007597449216 |