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researcher

S. S'anchez

3 papers hereh-index 343 citations3 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author1

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • eess.IV2
  • cs.AR1
same name
  • S. S'anchez — 55 papers, h 32
  • S. S'anchez — 7 papers, h 3
  • S. S'anchez — 6 papers, h 1
  • S. S'anchez — 4 papers, h 3
  • S. S'anchez — 3 papers, h 1
  • S. S'anchez — 2 papers, h 0

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedComparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients

14 citations · 21 across the 3 of their papers we have counts for

collaborators

3 papers

cs.AR2024★ 4 cited

Design and implementation of a synchronous Hardware Performance Monitor for a RISC-V space-oriented processor

Miguel Jiménez Arribas, Agustín Martínez Hellín, Manuel Prieto Mateo +6

The ability to collect statistics about the execution of a program within a CPU is of the utmost importance across all fields of computing since it allows characterizing the timing…

eess.IV2020★ 3 cited

Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging

Abel Díaz Berenguer, Hichem Sahli, Boris Joukovsky +37

Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised c…

eess.IV2020★ 14 cited

Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients

Sofie Tilborghs, Ine Dirks, Lucas Fidon +19

Recent research on COVID-19 suggests that CT imaging provides useful information to assess disease progression and assist diagnosis, in addition to help understanding the disease.…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.