Analysis of class agnostic explanations for the prediction of Diabetic Retinopathy
Optical Coherence Tomography Angiography (OCTA) is a promising new imaging technique in the field of ophthalmology for diagnosing Diabetic Retinopathy (DR) in diabetic patients. Early detection and treatment of DR is crucial to preventing vision loss and blindness. With rising numbers of diabetes ca...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/412464 |
| Acceso en línea: | https://hdl.handle.net/2117/412464 |
| Access Level: | acceso abierto |
| Palabra clave: | Neural networks (Computer science) Diabetic retinopathy CNN XAI OCTA DR Optical Coherence Tomography Angiography explainable artificial intelligence convolutional neural networks diabetic retinopathy signature activation saliency map Xarxes neuronals (Informàtica) Retinopatia diabètica Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | Optical Coherence Tomography Angiography (OCTA) is a promising new imaging technique in the field of ophthalmology for diagnosing Diabetic Retinopathy (DR) in diabetic patients. Early detection and treatment of DR is crucial to preventing vision loss and blindness. With rising numbers of diabetes cases around the world, it is more important than ever to correctly diagnose and categorize DR. Novel approaches to image classification, such as convolutional neural networks (CNN) have proven to be very effective with many types of images, however, their adoption in the medical field hinges on the ability of the medical professionals to understand how these networks make their decisions. This thesis focuses on exploring explainable artificial intelligence (XAI) techniques to better understand how a CNN can classify real OCTA images of diabetes patients with varying levels of DR. In particular, the primary technique explored is Signature Activation, which can generate holistic and class-agnostic explanations of the CNN's decisions. Over the course of this thesis several CNN models were obtained utilizing different architectures and training hyperparameters. In addition, several explainability visualization techniques were developed and applied to these models at various layers in order to study how information propagates through the layers of deep neural networks and analyze how they arrive at their final outputs. |
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