David Romo‐Bucheli

501 total citations
13 papers, 327 citations indexed

About

David Romo‐Bucheli is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Biophysics. According to data from OpenAlex, David Romo‐Bucheli has authored 13 papers receiving a total of 327 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Biophysics. Recurrent topics in David Romo‐Bucheli's work include AI in cancer detection (8 papers), Cell Image Analysis Techniques (5 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). David Romo‐Bucheli is often cited by papers focused on AI in cancer detection (8 papers), Cell Image Analysis Techniques (5 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). David Romo‐Bucheli collaborates with scholars based in Colombia, United States and Austria. David Romo‐Bucheli's co-authors include Hannah Gilmore, Anant Madabhushi, Andrew Janowczyk, Eduardo Romero, Hrvoje Bogunović, Ursula Schmidt‐Erfurth, Shridar Ganesan, David L. Rimm, Xiangxue Wang and Cheng Lu and has published in prestigious journals such as Scientific Reports, Laboratory Investigation and IEEE Journal of Biomedical and Health Informatics.

In The Last Decade

David Romo‐Bucheli

12 papers receiving 322 citations

Peers

Countries citing papers authored by David Romo‐Bucheli

Since Specialization

Citations

This map shows the geographic impact of David Romo‐Bucheli's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Romo‐Bucheli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Romo‐Bucheli more than expected).

Fields of papers citing papers by David Romo‐Bucheli

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Romo‐Bucheli. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Romo‐Bucheli. The network helps show where David Romo‐Bucheli may publish in the future.

Co-authorship network of co-authors of David Romo‐Bucheli

This figure shows the co-authorship network connecting the top 25 collaborators of David Romo‐Bucheli. A scholar is included among the top collaborators of David Romo‐Bucheli based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Romo‐Bucheli. David Romo‐Bucheli is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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13 of 13 papers shown

#	Work	Indexed citations
1	Deep learning representations to support COVID-19 diagnosis on CT slices 2022 ·Biomédica ·(unknown), (unknown), David Romo‐Bucheli, Carlos A. Vargas, (unknown), Óscar Mendoza, (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), Paul Anthony Camacho, Daniel Mantilla, Fabio Martínez	2
2	A digital cardiac disease biomarker from a generative progressive cardiac cine-MRI representation 2021 ·Biomedical Engineering Letters ·(unknown), David Romo‐Bucheli, Fabio Martínez	2
3	Cardiac Disease Representation Conditioned by Spatio-temporal Priors in Cine-MRI Sequences Using Generative Embedding Vectors 2021 ·2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) ·(unknown), (unknown), David Romo‐Bucheli, Fabio Martínez	1
4	A Covid-19 Patient Severity Stratification using a 3D Convolutional Strategy on CT-Scans 2021 ·(unknown), David Romo‐Bucheli, (unknown), (unknown), Carolina Valenzuela, (unknown), Paul Anthony Camacho, Daniel Mantilla, Fabio Martínez	1
5	End-to-End Deep Learning Model for Predicting Treatment Requirements in Neovascular AMD From Longitudinal Retinal OCT Imaging 2020 ·IEEE Journal of Biomedical and Health Informatics ·David Romo‐Bucheli, Ursula Schmidt‐Erfurth, Hrvoje Bogunović	50
6	Using Cyclegans for Effectively Reducing Image Variability Across OCT Devices and Improving Retinal Fluid Segmentation 2019 ·arXiv (Cornell University) ·Philipp Seeböck, David Romo‐Bucheli, Sebastian M. Waldstein, Hrvoje Bogunović, José Ignacio Orlando, Bianca S. Gerendas, Georg Langs, Ursula Schmidt‐Erfurth	5
7	Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina 2019 ·Biomedical Optics Express ·David Romo‐Bucheli, Philipp Seeböck, José Ignacio Orlando, Bianca S. Gerendas, Sebastian M. Waldstein, Ursula Schmidt‐Erfurth, Hrvoje Bogunović	41
8	Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers 2018 ·Laboratory Investigation ·Cheng Lu, David Romo‐Bucheli, Xiangxue Wang, Andrew Janowczyk, Shridar Ganesan, Hannah Gilmore, David L. Rimm, Anant Madabhushi	94
9	A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers 2017 ·Cytometry Part A ·David Romo‐Bucheli, Andrew Janowczyk, Hannah Gilmore, Eduardo Romero, Anant Madabhushi	46
10	Nuclei graph local features for basal cell carcinoma classification in whole slide images 2017 ·Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·David Romo‐Bucheli, Germán Corredor, (unknown), (unknown), Eduardo Romero	3
11	Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images 2016 ·Scientific Reports ·David Romo‐Bucheli, Andrew Janowczyk, Hannah Gilmore, Eduardo Romero, Anant Madabhushi	74
12	Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images 2016 ·Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·David Romo‐Bucheli, Andrew Janowczyk, Eduardo Romero, Hannah Gilmore, Anant Madabhushi	6
13	Identifying histological concepts on basal cell carcinoma images using nuclei based sampling and multi-scale descriptors 2015 ·David Romo‐Bucheli, (unknown), Ángel Cruz-Roa, Eduardo Romero	2

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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