no code implementations • 2 Nov 2023 • Anuja Vats, David Völgyes, Martijn Vermeer, Marius Pedersen, Kiran Raja, Daniele S. M. Fantin, Jacob Alexander Hay
We test the effectiveness of our approach by evaluating building segmentation performance on test datasets with varying label fractions.
1 code implementation • 26 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.
no code implementations • 16 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.
1 code implementation • 5 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.
3 code implementations • 11 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.
no code implementations • 10 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.
no code implementations • 30 Jun 2021 • Anuja Vats, Marius Pedersen, Ahmed Mohammed, Øistein Hovde
The progress in Computer Aided Diagnosis (CADx) of Wireless Capsule Endoscopy (WCE) is thwarted by the lack of data.
no code implementations • 8 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.
3 code implementations • 30 Jan 2020 • Max Allan, Satoshi Kondo, Sebastian Bodenstedt, Stefan Leger, Rahim Kadkhodamohammadi, Imanol Luengo, Felix Fuentes, Evangello Flouty, Ahmed Mohammed, Marius Pedersen, Avinash Kori, Varghese Alex, Ganapathy Krishnamurthi, David Rauber, Robert Mendel, Christoph Palm, Sophia Bano, Guinther Saibro, Chi-Sheng Shih, Hsun-An Chiang, Juntang Zhuang, Junlin Yang, Vladimir Iglovikov, Anton Dobrenkii, Madhu Reddiboina, Anubhav Reddy, Xingtong Liu, Cong Gao, Mathias Unberath, Myeonghyeon Kim, Chanho Kim, Chaewon Kim, Hye-Jin Kim, Gyeongmin Lee, Ihsan Ullah, Miguel Luna, Sang Hyun Park, Mahdi Azizian, Danail Stoyanov, Lena Maier-Hein, Stefanie Speidel
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models.
no code implementations • 25 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.
1 code implementation • Journal on Image and Video Processing 2018 • Ahmed Mohammed, Ivar Farup, Sule Yildirim, Marius Pedersen, Øistein Hovde
The main objective of the paper is to increase the frame rate to be closer to that of the colonoscopy.
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.
1 code implementation • 5 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.