Producción intelectual de David Edmundo Romo Bucheli

2023

Fast detection and localization of mitosis using a semi-supervised deep representation

etection, characterization and counting of mitosis is a main biomarker in cancer, allowing diagnosis, histological grading and prognosis. Nonetheless, mitosis identification remains as a challenging task (inter-observer variability up to 20%). Even, the computational support stra...


Autor(es): Santiago Andres Castro.   David Edmundo Romo.   Luis Carlos Guayacan.   Fabio Martinez.  



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2023

Prediction of Biomedical Waste Generation in Sanitary Emergencies for Urban Regions Using Multivariate Recurrent Neural Networks

Biomedical waste (BMW) generation is severely affected by generalized sanitary emergencies such as epidemics, as shown recently during the COVID-19 pandemic. These sanitary emergencies often increase plastic use in personal protection items, single-use plastics, and other healthc...


Autor(es): Nicolas Galvan.   David Felipe Rojas.   David Edmundo Romo.   Viatcheslav Kafarov.  



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2022

Computer Aided Assessment of impact of Plastic Waste During COVID-19 Pandemic in Urban Areas of Developing Countries.

Management of plastic waste during sanitary emergencies requires the development of computational tools to estimate its environmental impact. The purpose of this work is to develop a simulation model to predict the plastic waste trends during sanitary emergencies, such as the COV...


Autor(es): David Felipe Rojas.   Nicolas Galvan.   David Edmundo Romo.   Viatcheslav Kafarov.  



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2022

A digital cardiac disease biomarker from a generative progressive cardiac cine?MRI representation

Cardiac cine-MRI is one of the most important diagnostic tools used to assess the morphology and physiology of the heart during the cardiac cycle. Nonetheless, the analysis on cardiac cine-MRI is poorly exploited and remains highly dependent on the observer"s expertise. This work...


Autor(es): Santiago Gomez.   David Edmundo Romo.   Fabio Martinez.  



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2022

Deep learning representations to support COVID-19 diagnosis on CT slices

Introduction: The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding characterization, patient categoriz...


Autor(es): Jefferson David Rodriguez.   Miguel Alberto Plazas.   Lola Xiomara Bautista.   Jorge Villamizar.   Gabriel Rodrigo Pedraza.   Alejandra Moreno.   Fabio Martinez.   David Edmundo Romo.   Josúe Ruano.   John Arcila.   Carlos Vargas.   Oscar Mendoza.   Lina Vasquez.   Diana Valenzuela.   Carolina Valenzuela.   Paul Camacho.   Daniel Mantilla.  



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2021

Cardiac Disease Representation Conditioned by Spatio-temporal Priors in Cine-MRI Sequences Using Generative Embedding Vectors

Cardiac cine-MRI is one of the most important diagnostic tools for characterizing heart-related pathologies. This imaging technique allows clinicians to assess the morphology and physiology of the heart during the cardiac cycle. Nonetheless, the analysis on cardiac cine-MRI is hi...


Autor(es): Henry Ivan Peña.   Santiago Gomez.   David Edmundo Romo.   Fabio Martinez.  



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2021

A Covid-19 Patient Severity Stratification using a 3D Convolutional Strategy on CT-Scans

his work introduces a 3D deep learning methodology to stratify patients according to the severity of lung infection caused by COVID-19 disease on computerized tomography images (CT). A set of volumetric attention maps were also obtained to explain the results and support the diag...


Autor(es): Jefferson David Rodriguez.   David Edmundo Romo.   Franklin Samuel Sierra.   Fabio Martinez.   Diana Valenzuela.   Carolina Valenzuela.   Lina Vasquez.   Paul Camacho.   Daniel Mantilla.  



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2020

Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina

Diagnosis and treatment in ophthalmology depend on modern retinal imaging by optical coherence tomography (OCT). The recent staggering results of machine learning in medical imaging have inspired the development of automated segmentation methods to identify and quantify pathologi...


Autor(es): David Edmundo Romo.   Philipp Seebock.   José Ignacio Orlando.   Bianca S. Gerendas.   Sebastian M. Waldstein.   Ursula Schmidt-Erfurth.   Hrvoje Bogunovi?.  



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2020

End-to-End Deep Learning Model for Predicting Treatment Requirements in Neovascular AMD From Longitudinal Retinal OCT Imaging

Neovascular age-related macular degeneration (nAMD) is nowadays successfully treated with anti-VEGF substances, but inter-individual treatment requirements are vastly heterogeneous and currently poorly plannable resulting in suboptimal treatment frequency. Optical coherence tomog...


Autor(es): David Edmundo Romo.   Ursula Schmidt Erfurth.   Hrvoje Bogunovi?.  



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2018

Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers

Early-stage estrogen receptor-positive (ER+) breast cancer (BCa) is the most common type of BCa in the United States. One critical question with these tumors is identifying which patients will receive added benefit from adjuvant chemotherapy. Nuclear pleomorphism (variance in nuc...


Autor(es): David Edmundo Romo.   Cheng Lu.   Xiangxue Wang.   Andrew Janowczyk.   Shridar Ganesan.   Hannah Gilmore.   David Rimm.   Anant Madabhushi.  



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