Analysis by convolutional networks of histopathology images for genomic subtyping in breast cancer
| UDC.coleccion | Traballos académicos | es_ES |
| UDC.tipotrab | TFG | es_ES |
| UDC.titulacion | Grao en Enxeñaría Informática | es_ES |
| dc.contributor.advisor | Fernández-Lozano, Carlos | |
| dc.contributor.advisor | Liñares Blanco, José | |
| dc.contributor.author | Regal Llamas, Juan Miguel | |
| dc.contributor.other | Enxeñaría informática, Grao en | es_ES |
| dc.date.accessioned | 2022-08-12T08:41:51Z | |
| dc.date.available | 2022-08-12T08:41:51Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | [Abstract] Cancer is one of the main death causes in the world, being in 2020 almost 10 million deaths associated to this disease, this is one out of six deaths. Also, of the different types of cancer diagnosed, breast cancer was the most frequent in 2020, with 2.26 million cases detected. There exists cancer subtyping methods by genomic methods very accurate, however they have a very elevated cost, so being able to subtype with cheaper methods using clinical data and other data like images of tumor tissue samples would provide a low cost solution to subtype cancer. This project has as objective to implement a system for the download of data through the Genomic Data Commons (GDC) API, the preprocessing of images and clinical data, the application of different Convolutional Neural Network (CNN) models on images and clinical data, and the analysis and comparison of these results in a way that can be easily used with different data provided by the user or downloaded from the GDC, as also the use of this described program with breast cancer histopathological images and clinical data and the analysis of the results obtained. | es_ES |
| dc.description.abstract | [Resumo] O cancro é unha das principais causas de morte no mundo, sendo en 2020 casi 10 millones de mortes asociadas a esta enfermidade, esto é unha de cada seis mortes. Ademáis, dos diferentes tipos de cancro existentes diagnosticados, o de mama foi o mais frecuente en 2020, con 2,26 millóns de casos detectados. Existen métodos de subtipado de cancros por métodos xenómicos moi precisos, pero teñen un coste moi elevado, polo que poder subtipar mediante métodos máis baratos utilizando datos clínicos e outros datos coma imaxes de mostras de texido tumoral proporcionaría unha solución de baixo coste para subtipar cancro. Este traballo ten como obxectivo implementar un sistema de descarga de datos a través da API da Genomic Data Commons (GDC), o preprocesado das imaxes e dos datos clínicos, a aplicación de diferentes modelos de redes neuronales convolucionales (CNN) sobre imaxes e sobre datos clínicos, e o análise e comparación destes resultados de forma que se poida utilizar fácilmente con diferentes datos que o usuario proporcione ou descargue da GDC. Tamén a utilización deste programa descrito con imaxes histopatolóxicas de cancro de mama e datos clínicos e o análise dos resultados obtidos. | es_ES |
| dc.description.traballos | Traballo fin de grao. Enxeñaría Informática. Curso 2021/2022 | es_ES |
| dc.identifier.uri | http://hdl.handle.net/2183/31270 | |
| dc.language.iso | eng | es_ES |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
| dc.rights.accessRights | open access | es_ES |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | |
| dc.subject | Deep learning | es_ES |
| dc.subject | Breast cancer | es_ES |
| dc.subject | Histopathology images | es_ES |
| dc.subject | Genomic subtyping | es_ES |
| dc.title | Analysis by convolutional networks of histopathology images for genomic subtyping in breast cancer | es_ES |
| dc.type | bachelor thesis | |
| dspace.entity.type | Publication | |
| relation.isAdvisorOfPublication | e5ddd06a-3e7f-4bf4-9f37-5f1cf3d3430a | |
| relation.isAdvisorOfPublication | cf4ecc37-12be-45fc-add3-01c6a7f02630 | |
| relation.isAdvisorOfPublication.latestForDiscovery | e5ddd06a-3e7f-4bf4-9f37-5f1cf3d3430a |
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