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About the database ScholarIQanswers from OpenAlex & ORCID
How has Alfonso Medela's publication output changed over time?
ScholarIQpublication output · 2019–2023
Output grew100% over the shown period — from 1 works in 2019 to 2 in 2023.
1
2
2
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2
20192020202120222023
What are the most-cited papers on Alfonso Medela?
ScholarIQmost cited works
Few-Shot Learning approach for plant disease classification using images taken in the field
David Argüeso, Artzai Picón, Unai Irusta, Alfonso Medela, Miguel G. San-Emeterio, Arantza Bereciartua, Aitor Álvarez-Gila
Computers and Electronics in Agriculture. 2020365 Citations
Few Shot Learning in Histopathological Images:Reducing the Need of Labeled Data on Biological Datasets
Alfonso Medela, Artzai Picón, Cristina L. Saratxaga, Oihana Belar, Virginia Cabezón, Riccardo Cicchi, Roberto Bilbao, Ben Glover
201965 CitationsOPEN ACCESS
Automatic SCOring of Atopic Dermatitis Using Deep Learning: A Pilot Study
Alfonso Medela, Taig Mac Carthy, Andy Aguilar, Carlos M. Chiesa‐Estomba, Ramón Grimalt
S4210189051. 202241 CitationsOPEN ACCESS
Automatic International Hidradenitis Suppurativa Severity Score System (AIHS4): A novel tool to assess the severity of hidradenitis suppurativa using artificial intelligence
Ignacio Hernández Montilla, Alfonso Medela, Taig Mac Carthy, Andy Aguilar, Pedro Gómez Tejerina, Alejandro Vilas‐Sueiro, Ana María González Pérez, L. Vergara‐de‐la‐Campa, Loreto Luna Bastante, Rubén García Castro, Fernando Alfageme
S145688728. 202329 CitationsOPEN ACCESS
Constellation Loss: Improving the Efficiency of Deep Metric Learning Loss Functions for the Optimal Embedding of histopathological images
Alfonso Medela, Artzai Picón
S4210177785. 202024 CitationsOPEN ACCESS
Related on ScholarIQ
Andalusian Health Service
Institution
Few-Shot Learning approach for plant disease classification using images taken in the field
Paper
Few Shot Learning in Histopathological Images:Reducing the Need of Labeled Data on Biological Datasets
Paper
Automatic SCOring of Atopic Dermatitis Using Deep Learning: A Pilot Study
Paper
Automatic International Hidradenitis Suppurativa Severity Score System (AIHS4): A novel tool to assess the severity of hidradenitis suppurativa using artificial intelligence
Paper
Constellation Loss: Improving the Efficiency of Deep Metric Learning Loss Functions for the Optimal Embedding of histopathological images
Paper