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David Romo-Bucheli
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2020 – today
- 2024
- [c13]Juan Camilo Bayona Quesada, Angie Julieth Fuentes Barragán, Gabriel Eduardo Perez, David Romo-Bucheli:
Domain Adaptation Strategies Based on Color Normalisation for Histological Image Classification. SIPAIM 2024: 1-4 - [c12]Julián Orlando Rodriguez Villamizar, Daniel Felipe Rueda Mariño, David Romo-Bucheli:
Evaluating Regions of Clinical Interest Generated by Deep Recurrent Attention Models in Histological Images. SIPAIM 2024: 1-4 - 2023
- [c11]Santiago Castro, David Romo-Bucheli, Luis Guayacán, Fabio Martínez:
Fast detection and localization of mitosis using a semi-supervised deep representation. Digital and Computational Pathology 2023 - 2021
- [c10]Henry Peña, Santiago Gómez, David Romo-Bucheli, Fabio Martínez:
Cardiac Disease Representation Conditioned by Spatio-temporal Priors in Cine-MRI Sequences Using Generative Embedding Vectors. EMBC 2021: 5570-5573 - [c9]Jefferson Rodríguez, David Romo-Bucheli, Franklin Sierra, Diana Valenzuela, Carolina Valenzuela, Lina Vasquez, Paúl Camacho, Daniel Mantilla, Fabio Martínez:
A Covid-19 Patient Severity Stratification using a 3D Convolutional Strategy on CT-Scans. ISBI 2021: 1665-1668 - 2020
- [j1]David Romo-Bucheli, Ursula Schmidt-Erfurth, Hrvoje Bogunovic:
End-to-End Deep Learning Model for Predicting Treatment Requirements in Neovascular AMD From Longitudinal Retinal OCT Imaging. IEEE J. Biomed. Health Informatics 24(12): 3456-3465 (2020)
2010 – 2019
- 2019
- [c8]Philipp Seeböck, David Romo-Bucheli, Sebastian M. Waldstein, Hrvoje Bogunovic, José Ignacio Orlando, Bianca S. Gerendas, Georg Langs, Ursula Schmidt-Erfurth:
Using Cyclegans for Effectively Reducing Image Variability Across OCT Devices and Improving Retinal Fluid Segmentation. ISBI 2019: 605-609 - [i1]Philipp Seeböck, David Romo-Bucheli, Sebastian M. Waldstein, Hrvoje Bogunovic, José Ignacio Orlando, Bianca S. Gerendas, Georg Langs, Ursula Schmidt-Erfurth:
Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation. CoRR abs/1901.08379 (2019) - 2018
- [c7]Angélica Atehortúa, Mireille Garreau, David Romo-Bucheli, Eduardo Romero:
Automatic Left Ventricle Quantification in Cardiac MRI via Hierarchical Refinement of High-Level Features by a Salient Perceptual Grouping Model. STACOM@MICCAI 2018: 439-449 - 2017
- [c6]Javier Almeida, Josué Ruano, Germán Corredor, David Romo-Bucheli, José Ricardo Navarro-Vargas, Eduardo Romero:
Multi-channel non-invasive fetal electrocardiography detection using wavelet decomposition. SIPAIM 2017: 105720U - [c5]Ricardo Moncayo-Martínez, David Romo-Bucheli, Viviana Arias, Eduardo Romero:
Scoring nuclear pleomorphism using a visual BoF modulated by a graph structure. SIPAIM 2017: 1057205 - 2016
- [c4]David Romo-Bucheli, Andrew Janowczyk, Eduardo Romero, Hannah Gilmore, Anant Madabhushi:
Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images. Digital Pathology 2016: 979106 - 2015
- [c3]Ricardo Moncayo, David Romo-Bucheli, Eduardo Romero:
A Grading Strategy for Nuclear Pleomorphism in Histopathological Breast Cancer Images Using a Bag of Features (BOF). CIARP 2015: 75-82 - [c2]David Romo-Bucheli, Ricardo Moncayo, Angel Cruz-Roa, Eduardo Romero:
Identifying histological concepts on basal cell carcinoma images using nuclei based sampling and multi-scale descriptors. ISBI 2015: 1008-1011 - 2014
- [c1]David Romo, Juan D. García-Arteaga, Pablo Arbeláez, Eduardo Romero:
A discriminant multi-scale histopathology descriptor using dictionary learning. Digital Pathology 2014: 90410Q
Coauthor Index
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