Improving Breast Cancer Detection Using DCGANs Generated Synthetic Mammograms
Autor:
Garcia-Tiscar, Luis
Fecha:
11/09/2021Palabra clave:
Tipo de Ítem:
masterThesisResumen:
En el campo del Aprendizaje Automático, existe un compromiso entre la cantidad de datos con los que se entrena un modelo y los resultados del mismo. Uno de los principales problemas de la adquisición de datos son las anomalías, los valores erróneos o, principalmente, faltantes. En aplicaciones médicas, como el análisis de mamografías para la detección de tumores, estas anomalías pueden mermar el rendimiento del modelo y repercutir directamente en la vida de los pacientes, debido a un diagnóstico erróneo. Para subsanar este problema, presentamos un desarrollo de software capaz de generar mamografías sintéticas de alta resolución con presencia de tumores benignos y malignos a través de redes neuronales DCGAN (Deep Convolutional Generative Adversarial Networks). El objetivo es comprobar si estas imágenes sintéticas son clasificadas correctamente como tumores benignos o malignos por los clasificadores de cáncer de mama del estado del arte.
Descripción:
The amount of available training data and a Machine Learning model’s results are strongly related. Missing values are one of the main issues in this field, making models lose accuracy and overall quality in their results. Machine Learning models, especially in healthcare applications, need to be very accurate, because poor results lead to misdiagnosis and can affect patients' health. In order to solve this issue, we present an unsupervised ML model to generate high resolution synthetic mammograms based on DCGAN (Deep Convolutional Generative Adversarial Networks). Our goal is to create synthetic mammograms, indistinguishable from real ones, to feed these synthetic images to state-of-the-art breast cancer detection models and check if they are treated as real mammograms.
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