Polyp image segmentation with polyp2seg

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dc.contributor.advisorMontoya Zegarra, Javier Alexander
dc.contributor.authorMandujano Cornejo, Vittorino
dc.date.accessioned2023-11-27T15:00:28Z
dc.date.available2023-11-27T15:00:28Z
dc.date.issued2023
dc.description.abstractColorectal cancer (CRC) is the third most common type of cancer worldwide. It can be prevented by screening the colon and detecting polyps which might become malign. Therefore, an accurate diagnosis of polyps in colonoscopy images is crucial for CRC prevention. The introduction of computational techniques, well known as Computed Aided Diagnosis, facilitates diffusion and improves early recognition of potentially cancerous tissues. In this work, we propose a novel hybrid deep learning architecture for polyp image segmentation named Polyp2Seg. The model adopts a transformer architecture as its encoder to extract multi-hierarchical features. Additionally, a novel Feature Aggregation Module (FAM) merges progressively the multilevel features from the encoder to better localise polyps by adding semantic information. Next, a Multi-Context Attention Module (MCAM) removes noise and other artifacts, while incorporating a multi-scale attention mechanism to further improve polyp detection. Quantitative and qualitative experiments on five challenging datasets and over 5 different SOTAs demonstrate that our method significantly improves the segmentation accuracy of Polyps under different evaluation metrics. Our model achieves a new state-of the-art over most of the datasets.es_PE
dc.description.uriTesis de maestríaes_PE
dc.formatapplication/pdf
dc.identifier.other1080233
dc.identifier.urihttps://hdl.handle.net/20.500.12590/17849
dc.language.isoeng
dc.publisherUniversidad Católica San Pablo
dc.publisher.countryPE
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectDeep learninges_PE
dc.subjectComputer visiónes_PE
dc.subjectColo-rectal canceres_PE
dc.subjectImage Segmentationes_PE
dc.subjectMedical dataes_PE
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#1.02.01
dc.titlePolyp image segmentation with polyp2seges_PE
dc.typeinfo:eu-repo/semantics/masterThesis
dc.type.versioninfo:eu-repo/semantics/publishedVersion
renati.advisor.dni41312254
renati.advisor.orcidhttps://orcid.org/0000-0002-3652-1954
renati.author.dni70512168
renati.discipline611017
renati.jurorOchoa Luna, José Eduardo
renati.jurorDou, Qi
renati.jurorYang, Guang
renati.levelhttps://purl.org/pe-repo/renati/level#maestro
renati.typehttps://purl.org/pe-repo/renati/type#tesis
thesis.degree.disciplineCiencia de la Computaciónes_PE
thesis.degree.grantorUniversidad Católica San Pablo. Departamento de Ciencia de la Computación
thesis.degree.levelMaestríaes_PE
thesis.degree.nameMaestro en Ciencia de la Computaciónes_PE
thesis.degree.programEscuela Profesional Ciencia de la Computaciónes_PE
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