1,710 research outputs found
Diabetic Retinopathy Classification and Interpretation using Deep Learning Techniques
La retinopatia diabètica és una malaltia crònica i una de les principals causes de ceguesa i discapacitat visual en els
pacients diabètics. L'examen ocular a través d'imatges de la retina és utilitzat pels metges per detectar les lesions
relacionades amb aquesta malaltia. En aquesta tesi, explorem diferents mètodes innovadors per a la classificació
automàtica del grau de malaltia utilitzant imatges del fons d'ull. Per a aquest propòsit, explorem mètodes basats en
l'extracció i classificació automàtica, basades en xarxes neuronals profundes. A més, dissenyem un nou mètode per a
la interpretació dels resultats. El model està concebut de manera modular per a que pugui ser utilitzat en d'altres
xarxes i dominis de classificació. Demostrem experimentalment que el nostre model d'interpretació és capaç de
detectar lesions de retina a la imatge únicament a partir de la informació de classificació. A més, proposem un mètode
per comprimir la representació interna de la informació de la xarxa. El mètode es basa en una anàlisi de components
independents sobre la informació del vector d'atributs intern de la xarxa generat pel model per a cada imatge. Usant el
nostre mètode d'interpretació esmentat anteriorment també és possible visualitzar aquests components en la imatge.
Finalment, presentem una aplicació experimental del nostre millor model per classificar imatges de retina d'una
població diferent, concretament de l'Hospital de Reus. Els mètodes proposats arriben al nivell de rendiment de
l'oftalmòleg i són capaços d'identificar amb gran detall les lesions presents en les imatges, que es dedueixen només
de la informació de classificació de la imatge.La retinopatía diabética es una enfermedad crónica y una de las principales causas de ceguera y discapacidad visual
en los pacientes diabéticos. El examen ocular a través de imágenes de la retina es utilizado por los médicos para
detectar las lesiones relacionadas con esta enfermedad. En esta tesis, exploramos diferentes métodos novedosos
para la clasificación automática del grado de enfermedad utilizando imágenes del fondo de la retina. Para este
propósito, exploramos métodos basados en la extracción y clasificación automática, basadas en redes neuronales
profundas. Además, diseñamos un nuevo método para la interpretación de los resultados. El modelo está concebido
de manera modular para que pueda ser utilizado utilizando otras redes y dominios de clasificación. Demostramos
experimentalmente que nuestro modelo de interpretación es capaz de detectar lesiones de retina en la imagen
únicamente a partir de la información de clasificación. Además, proponemos un método para comprimir la
representación interna de la información de la red. El método se basa en un análisis de componentes independientes
sobre la información del vector de atributos interno de la red generado por el modelo para cada imagen. Usando
nuestro método de interpretación mencionado anteriormente también es posible visualizar dichos componentes en la
imagen. Finalmente, presentamos una aplicación experimental de nuestro mejor modelo para clasificar imágenes de
retina de una población diferente, concretamente del Hospital de Reus. Los métodos propuestos alcanzan el nivel de
rendimiento del oftalmólogo y son capaces de identificar con gran detalle las lesiones presentes en las imágenes, que
se deducen solo de la información de clasificación de la imagen.Diabetic Retinopathy is a chronic disease and one of the main causes of blindness and visual impairment for diabetic
patients. Eye screening through retinal images is used by physicians to detect the lesions related with this disease. In
this thesis, we explore different novel methods for the automatic diabetic retinopathy disease grade classification using
retina fundus images. For this purpose, we explore methods based in automatic feature extraction and classification,
based on deep neural networks. Furthermore, as results reported by these models are difficult to interpret, we design
a new method for results interpretation. The model is designed in a modular manner in order to generalize its possible
application to other networks and classification domains. We experimentally demonstrate that our interpretation model
is able to detect retina lesions in the image solely from the classification information. Additionally, we propose a
method for compressing model feature-space information. The method is based on a independent component analysis
over the disentangled feature space information generated by the model for each image and serves also for identifying
the mathematically independent elements causing the disease. Using our previously mentioned interpretation method
is also possible to visualize such components on the image. Finally, we present an experimental application of our
best model for classifying retina images of a different population, concretely from the Hospital de Reus. The methods
proposed, achieve ophthalmologist performance level and are able to identify with great detail lesions present on
images, inferred only from image classification information
Neural network for ordinal classification of imbalanced data by minimizing a Bayesian cost
Ordinal classification of imbalanced data is a challenging problem that appears in many real world applications. The challenge is to simultaneously consider the order of the classes and the class imbalance, which can notably improve the performance metrics. The Bayesian formulation allows to deal with these two characteristics jointly: It takes into account the prior probability of each class and the decision costs, which can be used to include the imbalance and the ordinal information, respectively. We propose to use the Bayesian formulation to train neural networks, which have shown excellent results in many classification tasks. A loss function is proposed to train networks with a single neuron in the output layer and a threshold based decision rule. The loss is an estimate of the Bayesian classification cost, based on the Parzen windows estimator, which is fitted for a thresholded decision. Experiments with several real datasets show that the proposed method provides competitive results in different scenarios, due to its high flexibility to specify the relative importance of the errors in the classification of patterns of different classes, considering the order and independently of the probability of each class.This work was partially supported by Spanish Ministry of Science and Innovation through Thematic Network "MAPAS"(TIN2017-90567-REDT) and by BBVA Foundation through "2-BARBAS" research grant. Funding for APC: Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2023)
Error-Correcting Output Codes in the Framework of Deep Ordinal Classification
Automatic classification tasks on structured data have been revolutionized by Convolutional Neural Networks (CNNs), but the focus has been on binary and nominal classification tasks. Only recently, ordinal classification (where class labels present a natural ordering) has been tackled through the framework of CNNs. Also, ordinal classification datasets commonly present a high imbalance in the number of samples of each class, making it an even harder problem. Focus should be shifted from classic classification metrics towards per-class metrics (like AUC or Sensitivity) and rank agreement metrics (like Cohen’s Kappa or Spearman’s rank correlation coefficient). We present a new CNN architecture based on the Ordinal Binary Decomposition (OBD) technique using Error-Correcting Output Codes (ECOC). We aim to show experimentally, using four different CNN architectures and two ordinal classification datasets, that the OBD+ECOC methodology significantly improves the mean results on the relevant ordinal and class-balancing metrics. The proposed method is able to outperform a nominal approach as well as already existing ordinal approaches, achieving a mean performance of RMSE=1.0797 for the Retinopathy dataset and RMSE=1.1237 for the Adience dataset averaged over 4 different architectures
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