Zobrazeno 1 - 10
of 635
pro vyhledávání: '"Zinkernagel, Martin"'
Autor:
Pissas, Theodoros, Márquez-Neila, Pablo, Wolf, Sebastian, Zinkernagel, Martin, Sznitman, Raphael
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 scalable method for self-supervised learning, to obtain a powerful and
Externí odkaz:
http://arxiv.org/abs/2405.14788
Autor:
Ghamsarian, Negin, El-Shabrawi, Yosuf, Nasirihaghighi, Sahar, Putzgruber-Adamitsch, Doris, Zinkernagel, Martin, Wolf, Sebastian, Schoeffmann, Klaus, Sznitman, Raphael
In recent years, the landscape of computer-assisted interventions and post-operative surgical video analysis has been dramatically reshaped by deep-learning techniques, resulting in significant advancements in surgeons' skills, operation room managem
Externí odkaz:
http://arxiv.org/abs/2312.06295
Autor:
Ghamsarian, Negin, Wolf, Sebastian, Zinkernagel, Martin, Schoeffmann, Klaus, Sznitman, Raphael
Semantic Segmentation plays a pivotal role in many applications related to medical image and video analysis. However, designing a neural network architecture for medical image and surgical video segmentation is challenging due to the diverse features
Externí odkaz:
http://arxiv.org/abs/2312.03409
Autor:
Ghamsarian, Negin, Tejero, Javier Gamazo, Neila, Pablo Márquez, Wolf, Sebastian, Zinkernagel, Martin, Schoeffmann, Klaus, Sznitman, Raphael
Models capable of leveraging unlabelled data are crucial in overcoming large distribution gaps between the acquired datasets across different imaging devices and configurations. In this regard, self-training techniques based on pseudo-labeling have b
Externí odkaz:
http://arxiv.org/abs/2307.16660
Autor:
Jungo, Alain, Doorenbos, Lars, Da Col, Tommaso, Beelen, Maarten, Zinkernagel, Martin, Márquez-Neila, Pablo, Sznitman, Raphael
Purpose: A fundamental problem in designing safe machine learning systems is identifying when samples presented to a deployed model differ from those observed at training time. Detecting so-called out-of-distribution (OoD) samples is crucial in safet
Externí odkaz:
http://arxiv.org/abs/2304.05040
Autor:
Tejero, Javier Gamazo, Zinkernagel, Martin S., Wolf, Sebastian, Sznitman, Raphael, Neila, Pablo Márquez
Annotating new datasets for machine learning tasks is tedious, time-consuming, and costly. For segmentation applications, the burden is particularly high as manual delineations of relevant image content are often extremely expensive or can only be do
Externí odkaz:
http://arxiv.org/abs/2303.11678
Autor:
Marafioti, Andrés, Hayoz, Michel, Gallardo, Mathias, Neila, Pablo Márquez, Wolf, Sebastian, Zinkernagel, Martin, Sznitman, Raphael
Cataract surgery is a sight saving surgery that is performed over 10 million times each year around the world. With such a large demand, the ability to organize surgical wards and operating rooms efficiently is critical to delivery this therapy in ro
Externí odkaz:
http://arxiv.org/abs/2106.11048
Autor:
Wendelstein, Jascha A., Hoffmann, Peter C., Hoffer, Kenneth J., Langenbucher, Achim, Findl, Oliver, Ruiss, Manuel, Bolz, Matthias, Riaz, Kamran M., Pantanelli, Seth M., Debellemanière, Guillaume, Gatinel, Damien, Cooke, David L., Galzignato, Alice, Yeo, Tun Kuan, Seiler, Theo G., Zinkernagel, Martin, Savini, Giacomo
Publikováno v:
In American Journal of Ophthalmology April 2024 260:102-114
Autor:
Ferro Desideri, Lorenzo, Sim, Peng Yong, Bernardi, Enrico, Paschon, Karin, Roth, Janice, Fung, Adrian T., Wu, Xia Ni, Chou, Hung-Da, Henderson, Robert, Tsui, Edmund, Berrocal, Maria, Chhablani, Jay, Wykoff, Charles C., Cheung, Chui Ming Gemmy, Querques, Giuseppe, Melo, Gustavo Barreto, Subhi, Yousif, Loewenstein, Anat, Kiilgaard, Jens Folke, Zinkernagel, Martin, Anguita, Rodrigo
Publikováno v:
In Survey of Ophthalmology September 2024