◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Omar S. M. El Nahhas

7 papers here

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

author position
  • first author1
  • middle author5

Across the 6 of 7 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • eess.IV2
  • cs.AI1
  • cs.LG1
ORCID 0000-0002-2542-2117

identity via Semantic Scholar / OpenAlex

most citedBenchmarking foundation models as feature extractors for weakly-supervised computational pathology

13 citations · 34 across the 7 of their papers we have counts for

collaborators
Showing cs.LGShow all

1 paper · 1 filter

cs.LG2024

Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology

Tim Lenz, Omar S. M. El Nahhas, Marta Ligero +1

Deep Learning models have been successfully utilized to extract clinically actionable insights from routinely available histology data. Generally, these models require annotations…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.