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Marc Pouly

Lucerne University of Applied Sciences

17 papers hereh-index 12572 citations70 works total

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

author position
  • middle author8
  • last author9

Across the 17 of 17 papers where every author was matched, so the position is known.

fields
  • cs.CV10
  • cs.AI3
  • cs.CL2
  • cs.DL1
  • cs.SD1
affiliations
  • Lucerne University of Applied Sciences
same name
  • Marc Pouly — 3 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

activity
20152026
most citedTowards Reliable Dermatology Evaluation Benchmarks

4 citations · 11 across the 15 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CV2024

Towards Scalable Foundation Models for Digital Dermatology

Fabian Gröger, Philippe Gottfrois, Ludovic Amruthalingam +5

The growing demand for accurate and equitable AI models in digital dermatology faces a significant challenge: the lack of diverse, high-quality labeled data. In this work, we inves…

cs.CV2024★ 3 cited

PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa

Philippe Gottfrois, Fabian Gröger, Faly Herizo Andriambololoniaina +13

Africa faces a huge shortage of dermatologists, with less than one per million people. This is in stark contrast to the high demand for dermatologic care, with 80% of the paediatri…

cs.CV2024

Hyperbolic Metric Learning for Visual Outlier Detection

Alvaro Gonzalez-Jimenez, Simone Lionetti, Dena Bazazian +4

Out-Of-Distribution (OOD) detection is critical to deploy deep learning models in safety-critical applications. However, the inherent hierarchical concept structure of visual data,…

cs.CL2024

Estimating Text Similarity based on Semantic Concept Embeddings

Tim vor der Brück, Marc Pouly

Due to their ease of use and high accuracy, Word2Vec (W2V) word embeddings enjoy great success in the semantic representation of words, sentences, and whole documents as well as fo…

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