cv

L3 Machine Learning Engineer

Kueski

Professor @ Deep Learning Course

Universidad de Buenos Aires

L2 Machine Learning Engineer

Kueski

Sr Data Scientist

Mercado Libre

Ssr Data Scientist

Mercado Libre

Web Developer

Serfe

Data Scientist

Eiwa

Eng

ITBA

Cloud Data Engineering

Eng

Universidad Nacional del Litoral

Informatics Engineering

• Calculus
• Linear Algrebra
• Programming
• Statistics
• Physics
• Computational Intelligence
• Digital Signal Processing
• Digital Image Processing
• Computational Mechanics
• Computer Graphics
• Networks
• Robotics

Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data

IEEE Journal of Biomedical and Health Informatics

Automatic quantification of the left ventricle (LV) from cardiac magnetic resonance (CMR) images plays an important role in making the diagnosis procedure efficient, reliable, and alleviating the laborious reading work for physicians. Considerable efforts have been devoted to LV quantification using different strategies that include segmentation-based (SG) methods and the recent direct regression (DR) methods. Although both SG and DR methods have obtained great success for the task, a systematic platform to benchmark them remains absent because of differences in label information during model learning. In this paper, we conducted an unbiased evaluation and comparison of cardiac LV quantification methods that were submitted to the Left Ventricle Quantification (LVQuan) challenge, which was held in conjunction with the Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop at the MICCAI 2018.

Left ventricle quantification through spatio-temporal CNNs

Springer

Cardiovascular diseases are among the leading causes of death globally. Cardiac left ventricle (LV) quantification is known to be one of the most important tasks for the identification and diagnosis of such pathologies. In this paper, we propose a deep learning method that incorporates 3D spatio-temporal convolutions to perform direct left ventricle quantification from cardiac MR sequences. Instead of analysing slices independently, we process stacks of temporally adjacent slices by means of 3D convolutional kernels which fuse the spatio-temporal information, incorporating the temporal dynamics of the heart to the learned model. We show that incorporating such information by means of spatio-temporal convolutions into standard LV quantification architectures improves the accuracy of the predictions when compared with single-slice models, achieving competitive results for all cardiac indices and significantly breaking the state of the art (Xue et al., 2018, MedIA) for cardiac phase estimation.

Cuantificación del ventrículo cardíaco izquierdo mediante redes neuronales convolucionales espacio-temporales

Encuentro de Jóvenes Investigadores - Universidad Nacional del Litoral