no code implementations • 30 Mar 2017 • Iro Laina, Nicola Rieke, Christian Rupprecht, Josué Page Vizcaíno, Abouzar Eslami, Federico Tombari, Nassir Navab
Real-time instrument tracking is a crucial requirement for various computer-assisted interventions.
no code implementations • 18 Feb 2018 • Jakob Weiss, Nicola Rieke, Mohammad Ali Nasseri, Mathias Maier, Abouzar Eslami, Nassir Navab
We propose to build on its desirable qualities and present a method for tracking the orientation and location of a surgical needle.
no code implementations • 17 Aug 2018 • Mingchuan Zhou, Mahdi Hamad, Jakob Weiss, Abouzar Eslami, Kai Huang, Mathias Maier, Chris P. Lohmann, Nassir Navab, Alois Knoll, M. Ali Nasseri
Ophthalmic microsurgery is known to be a challenging operation, which requires very precise and dexterous manipulation.
no code implementations • 17 Apr 2019 • Mhd Hasan Sarhan, Abouzar Eslami, Nassir Navab, Shadi Albarqouni
Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice.
no code implementations • 18 Apr 2019 • Mhd Hasan Sarhan, Shadi Albarqouni, Mehmet Yigitsoy, Nassir Navab, Abouzar Eslami
To enhance the discriminative power of the classification model, we incorporate triplet embedding loss with a selective sampling routine.
1 code implementation • 10 Dec 2019 • Mert Kayhan, Okan Köpüklü, Mhd Hasan Sarhan, Mehmet Yigitsoy, Abouzar Eslami, Gerhard Rigoll
To this end, a lightweight network architecture is introduced and mean teacher, virtual adversarial training and pseudo-labeling algorithms are evaluated on 2D-pose estimation for surgical instruments.
1 code implementation • ECCV 2020 • Mhd Hasan Sarhan, Nassir Navab, Abouzar Eslami, Shadi Albarqouni
We explicitly enforce the meaningful representation to be agnostic to sensitive information by entropy maximization.
1 code implementation • 7 Jul 2022 • Tobias Hänel, Nishant Kumar, Dmitrij Schlesinger, Mengze Li, Erdem Ünal, Abouzar Eslami, Stefan Gumhold
The performance of deep neural networks for image recognition tasks such as predicting a smiling face is known to degrade with under-represented classes of sensitive attributes.
1 code implementation • CVPR 2023 • Nishant Kumar, Siniša Šegvić, Abouzar Eslami, Stefan Gumhold
However, this strategy does not guarantee that the synthesized outlier features will have a low likelihood according to the other class-conditional Gaussians.
1 code implementation • 20 Aug 2023 • Masoud Taghikhah, Nishant Kumar, Siniša Šegvić, Abouzar Eslami, Stefan Gumhold
Previous attempts to address this challenge involved training image classifiers through contrastive learning using actual outlier data or synthesizing outliers for self-supervised learning.