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Theodoros Pissas

Novartis

5 papers hereh-index 8196 citations14 works total

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

author position
  • first author3
  • middle author2

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • eess.IV1
affiliations
  • Novartis
  • past University College London and Kings College London and University of Bern
ORCID 0000-0001-9500-7085

identity via Semantic Scholar / OpenAlex

activity
20212024
most citedStochastic Segmentation with Conditional Categorical Diffusion Models

2 citations · 2 across the 4 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2024

Masked Image Modelling for retinal OCT understanding

Theodoros Pissas, Pablo Márquez-Neila, Sebastian Wolf +2

This work explores the effectiveness of masked image modelling for learning representations of retinal OCT images. To this end, we leverage Masked Autoencoders (MAE), a simple and…

cs.CV2023★ 2 cited

Stochastic Segmentation with Conditional Categorical Diffusion Models

Lukas Zbinden, Lars Doorenbos, Theodoros Pissas +3

Semantic segmentation has made significant progress in recent years thanks to deep neural networks, but the common objective of generating a single segmentation output that accurat…

cs.CV2022

Multi-scale and Cross-scale Contrastive Learning for Semantic Segmentation

Theodoros Pissas, Claudio S. Ravasio, Lyndon Da Cruz +1

This work considers supervised contrastive learning for semantic segmentation. We apply contrastive learning to enhance the discriminative power of the multi-scale features extract…

cs.CV2021

Effective semantic segmentation in Cataract Surgery: What matters most?

Theodoros Pissas, Claudio Ravasio, Lyndon Da Cruz +1

Our work proposes neural network design choices that set the state-of-the-art on a challenging public benchmark on cataract surgery, CaDIS. Our methodology achieves strong performa…

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