Esteban Torche

475 citations
11 papers · 291 · 1 hit paper · h-index 5

Impact in

    • Artificial Intelligence in Healthcare and Education
    • Brain Tumor Detection and Classification
    • Intracranial Aneurysms: Treatment and Complications

Papers in

Esteban Torche

11 papers receiving 285 citations

Esteban Torche's Hit Papers

Federated learning enables big data for rare cancer boundary detection 2022 · 251 citations
2510+1+2Years since publication50100150200250

Peers

Esteban Torche
Comparison fields: 5 of 57
  • Health Informatics 40
  • Neurology 29
  • Radiology, Nuclear Medicine and Imaging 76
  • Artificial Intelligence 107
  • Genetics 18
Replace Umang Pandey with:
Umang Pandey India
Peter Zampakis Greece
Panagiotis Tsiganos Greece
Kavi Fatania United Kingdom
Marek Dostál Czechia
Carmen Dragos United Kingdom
Derrick Murcia United States
Jacob Peoples Canada
Satyam Ghodasara United States
Ana Lorena Abello United States
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Countries citing papers authored by Esteban Torche

Since Specialization
Citations

This map shows the geographic impact of Esteban Torche'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 Esteban Torche with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Esteban Torche more than expected).

Fields of papers citing papers by Esteban Torche

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Esteban Torche. 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 Esteban Torche. The network helps show where Esteban Torche may publish in the future.

Co-authors

The 14 scholars most cited alongside Esteban Torche, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Esteban Torche Line = papers co-authored together Esteban Torche links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1
Federated learning enables big data for rare cancer boundary detection
Hit paper breakdown →
2022251
2 20197
3 20245
4 20115
5 20255
6 20134
7 20204
8 20144
9 20192
10 20132
11 20232

About Esteban Torche

Esteban Torche is a scholar working on Neurology, Pulmonary and Respiratory Medicine, Health Informatics, Radiology, Nuclear Medicine and Imaging and Surgery, having authored 11 papers that have together received 291 indexed citations. Recurring topics across this work include Intracranial Aneurysms: Treatment and Complications (5 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Cerebrovascular and Carotid Artery Diseases (2 papers), Cerebrospinal fluid and hydrocephalus (1 paper), Moyamoya disease diagnosis and treatment (1 paper), Aortic aneurysm repair treatments (1 paper), Spine and Intervertebral Disc Pathology (1 paper) and Intraoperative Neuromonitoring and Anesthetic Effects (1 paper). The work is most often cited by research in Health Informatics (40 citations), Neurology (29 citations), Radiology, Nuclear Medicine and Imaging (76 citations), Artificial Intelligence (107 citations) and Genetics (18 citations). Esteban Torche has collaborated with scholars based in Chile, France and Italy. Frequent co-authors include F. Riquelme, Jorge M. Mura, Eduardo López, Evandro de Oliveira, Juan Pablo Cabrera, Omer Eker, Gustavo Rassier Isolan, Nelson Wohllk, M. Loreto Spencer and Francis Turjman. Their work appears in journals such as Nature Communications, Journal of NeuroInterventional Surgery, Child s Nervous System, Neuroradiology and Surgical Neurology International.

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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