Santiago Toledo Machine Learning Research

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Deep Density Estimation for Cone Counting and Diagnosis of Genetic Eye Diseases From Adaptive Optics Scanning Light Opht

Trans Vis Sci Tech

Nov 2023

We introduce Cone Density Estimation (CoDE) and CoDE for Diagnosis (CoDED). CoDE is a deep density estimation model for cone counting that estimates a density function whose integral is equal to the number of cones. CoDED is an integration of CoDE with deep image classifiers for diagnosis. We use two AOSLO image datasets to train and evaluate the performance of cone density estimation and classification models for retinitis pigmentosa and Stargardt's disease.

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Characterization of Electricity Demand Based on Energy Consumption Data from Colombia

International Journal of Electrical and Computer Engineering IJECE

Jul 2023

Toledo-Cortés, S., Lara, J.S., Zambrano, Á., Gonzalez, F.A., Rosero-García, J. The development of dynamic energy distribution grids to optimize energy resources has become very important at the international level in recent years. A very important step in this development is to be able to characterize the population based on their consumption behaviour. However, traditional consumption meters that report information at a monthly rate provide little information for in-depth analysis. In Colombia, this has changed in recent years due to the implementation and integration of advanced metering infrastructure (AMI). This infrastructure allows to record consumption values in small time intervals, and the available data then allows for the execu

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Learning with Density Matrices and Random Features

Quantum Machine Intelligence

Aug 2022

Gonzalez, F.A., Gallego, A., Toledo-Cortés, S., Vargas-Calderon, V. A density matrix describes the statistical state of a quantum system. It is a powerful formalism to represent both the quantum and classical uncertainty of quantum systems and to express different statistical operations such as measurement, system combination and expectations as linear algebra operations. This paper explores how density matrices can be used as a building block to build machine learning models exploiting their ability to straightforwardly combine linear algebra and probability. One of the main results of the paper is to show that density matrices coupled with random Fourier features could approximate arbitrary probability distributions over Rn. Based on thi

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Grading diabetic retinopathy and prostate cancer diagnostic images with deep quantum ordinal regression

Computers in Biology and Medicine

Apr 2022

Santiago Toledo-Cortés, Diego H. Useche, Henning Müller, Fabio A. González Although for many diseases there is a progressive diagnosis scale, automatic analysis of grade-based medical images is quite often addressed as a binary classification problem, missing the finer distinction and intrinsic relation between the different possible stages or grades. Ordinal regression (or classification) considers the order of the values of the categorical labels and thus takes into account the order of grading scales used to assess the severity of different medical conditions. This paper presents a quantum-inspired deep probabilistic learning ordinal regression model for medical image diagnosis that takes advantage of the representational power of deep

Implementation of Deep Quantum Ordinal Regressor for Prostate Cancer and Diabetic Retinopathy Grading.

GitHub

Apr 2022

Toledo-Cortés S., Useche D.H., Müller H., González F.A.

Implementation of CODE and CODED for AOSLO Image Analysis

GitHub

Jan 2022

Toledo-Cortés S., Dubis, A. M., González, F.A., Müller, H.

Implementation of Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis.

GitHub

Oct 2021

Toledo-Cortés S., de la Pava M., Perdomo O., González F.A.

What You Need to Know About Artificial Intelligence: Technical Introduction

Springer

Apr 2021

Oscar J. Perdomo, Santiago Toledo-Cortés, Alvaro Orjuela & Fabio A. González The general concepts of deep learning, machine learning, and artificial intelligence (AI) are presented in this chapter, and the most representative techniques are briefly discussed. The chapter focuses on the technical details of AI and presents the performance obtained compared to human graders in retinal disease classification. Additionally, the different learning techniques, the main methods, and how these techniques are invaluable tools to support clinical decision-making are discussed.

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Prostate Tissue Grading with Deep Quantum Measurement Ordinal Regression

Mar 2021

Toledo-Cortés, S., Useche, D.H., Gonzalez, F.A. Prostate cancer (PCa) is one of the most common and aggressive cancers worldwide. The Gleason score (GS) system is the standard way of classifying prostate cancer and the most reliable method to determine the severity and treatment to follow. The pathologist looks at the arrangement of cancer cells in the prostate and assigns a score on a scale that ranges from 6 to 10. Automatic analysis of prostate whole-slide images (WSIs) is usually addressed as a binary classification problem, which misses the finer distinction between stages given by the GS. This paper presents a probabilistic deep learning ordinal classification method that can estimate the GS from a prostate WSI. Approaching the probl

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Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification

MICCAI Workshop on Ophthalmic Medical Image Analysis OMIA

Oct 2020

Toledo-Cortés S., de la Pava M., Perdomo O., González F.A. Diabetic Retinopathy (DR) is one of the microvascular complications of Diabetes Mellitus, which remains as one of the leading causes of blindness worldwide. Computational models based on Convolutional Neural Networks represent the state of the art for the automatic detection of DR using eye fundus images. Most of the current work address this problem as a binary classification task. However, including the grade estimation and quantification of predictions uncertainty can potentially increase the robustness of the model. In this paper, a hybrid Deep Learning-Gaussian process method for DR diagnosis and uncertainty quantification is presented. This method combines the representationa

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