Search Results for author: Marius Pedersen

Found 13 papers, 7 papers with code

Learning Pairwise Interaction for Generalizable DeepFake Detection

1 code implementation26 Feb 2023 Ying Xu, Kiran Raja, Luisa Verdoliva, Marius Pedersen

We obtain 98. 48% BOSC accuracy on the FF++ dataset and 90. 87% BOSC accuracy on the CelebDF dataset suggesting a promising direction for generalization of DeepFake detection.

Decision Making DeepFake Detection +2

Evaluating clinical diversity and plausibility of synthetic capsule endoscopic images

no code implementations16 Jan 2023 Anuja Vats, Marius Pedersen, Ahmed Mohammed, Øistein Hovde

Like training in other modalities such as traditional endoscopy, CT, MRI, etc., a WCE training protocol would require an atlas comprising of a large corpora of images that show vivid descriptions of pathologies and abnormalities, ideally observed over a period of time.

This changes to that : Combining causal and non-causal explanations to generate disease progression in capsule endoscopy

1 code implementation5 Dec 2022 Anuja Vats, Ahmed Mohammed, Marius Pedersen, Nirmalie Wiratunga

Due to the unequivocal need for understanding the decision processes of deep learning networks, both modal-dependent and model-agnostic techniques have become very popular.

Decision Making

Analyzing Fairness in Deepfake Detection With Massively Annotated Databases

3 code implementations11 Aug 2022 Ying Xu, Philipp Terhörst, Kiran Raja, Marius Pedersen

In this work, we investigate factors causing biased detection in public Deepfake datasets by (a) creating large-scale demographic and non-demographic attribute annotations with 47 different attributes for five popular Deepfake datasets and (b) comprehensively analysing attributes resulting in AI-bias of three state-of-the-art Deepfake detection backbone models on these datasets.

Attribute Decision Making +3

From Labels to Priors in Capsule Endoscopy: A Prior Guided Approach for Improving Generalization with Few Labels

no code implementations10 Jun 2022 Anuja Vats, Ahmed Mohammed, Marius Pedersen

Prior works have considered using higher quality and quantity of labels as a way of tackling the lack of generalization, however this is hardly scalable considering pathology diversity not to mention that labeling large datasets encumbers the medical staff additionally.

Management

Advanced Image Enhancement Method for Distant Vessels and Structures in Capsule Endoscopy

no code implementations8 Apr 2021 Olivier Rukundo, Marius Pedersen, Øistein Hovde

The proposed method (PM) combines two algorithms for the enhancement of darker and brighter areas of capsule endoscopic images, respectively.

Image Enhancement

A Three-Feature Model to Predict Colour Change Blindness

no code implementations25 Aug 2019 Steven Le Moan, Marius Pedersen

Change blindness is a striking shortcoming of our visual system which is exploited in the popular "Spot the difference" game.

regression

Stochastic Capsule Endoscopy Image Enhancement

1 code implementation Journal of Imaging 2018 Ahmed Mohammed, Ivar Farup, Marius Pedersen, Øistein Hovde, and Sule Yildirim Yayilgan

In this paper, we consider the problem of enhancing the visibility of detail and shadowed tissue surfaces for capsule endoscopy images.

Image Enhancement

Y-Net: A deep Convolutional Neural Network for Polyp Detection

1 code implementation5 Jun 2018 Ahmed Mohammed, Sule Yildirim, Ivar Farup, Marius Pedersen, Øistein Hovde

To handle this problem, we propose a novel deep learning method Y-Net that consists of two encoder networks with a decoder network.

Cannot find the paper you are looking for? You can Submit a new open access paper.