no code implementations • 3 Nov 2023 • Pavel Jahoda, Azade Farshad, Yousef Yeganeh, Ehsan Adeli, Nassir Navab
We take advantage of the outer part of the masked area as they have a direct correlation with the context of the scene.
no code implementations • 28 Apr 2023 • Azade Farshad, Yousef Yeganeh, Yu Chi, Chengzhi Shen, Björn Ommer, Nassir Navab
To address this limitation, we propose a novel guidance approach for the sampling process in the diffusion model that leverages bounding box and segmentation map information at inference time without additional training data.
no code implementations • 28 Apr 2023 • Yousef Yeganeh, Azade Farshad, Goktug Guevercin, Amr Abu-zer, Rui Xiao, Yongjian Tang, Ehsan Adeli, Nassir Navab
Although the preservation of shape continuity and physiological anatomy is a natural assumption in the segmentation of medical images, it is often neglected by deep learning methods that mostly aim for the statistical modeling of input data as pixels rather than interconnected structures.
no code implementations • 28 Apr 2023 • Yousef Yeganeh, Azade Farshad, Peter Weinberger, Seyed-Ahmad Ahmadi, Ehsan Adeli, Nassir Navab
Although purely transformer-based architectures showed promising performance in many computer vision tasks, many hybrid models consisting of CNN and transformer blocks are introduced to fit more specialized tasks.
no code implementations • 10 Nov 2022 • Azade Farshad, Yousef Yeganeh, Helisa Dhamo, Federico Tombari, Nassir Navab
Graph representation of objects and their relations in a scene, known as a scene graph, provides a precise and discernible interface to manipulate a scene by modifying the nodes or the edges in the graph.
no code implementations • 12 Jul 2022 • Yousef Yeganeh, Azade Farshad, Nassir Navab
Inpainting has recently been proposed as a successful deep learning technique for unsupervised medical image model discovery.
no code implementations • 7 Jul 2022 • Yousef Yeganeh, Azade Farshad, Johann Boschmann, Richard Gaus, Maximilian Frantzen, Nassir Navab
Although most medical centers conduct similar medical imaging tasks, their differences, such as specializations, number of patients, and devices, lead to distinctive data distributions.
1 code implementation • 15 Apr 2022 • Azade Farshad, Yousef Yeganeh, Peter Gehlbach, Nassir Navab
Automated segmentation of retinal optical coherence tomography (OCT) images has become an important recent direction in machine learning for medical applications.
Ranked #1 on Retinal OCT Layer Segmentation on Duke SD-OCT (using extra training data)
1 code implementation • 22 Oct 2021 • Azade Farshad, Sabrina Musatian, Helisa Dhamo, Nassir Navab
We propose MIGS (Meta Image Generation from Scene Graphs), a meta-learning based approach for few-shot image generation from graphs that enables adapting the model to different scenes and increases the image quality by training on diverse sets of tasks.
1 code implementation • NeurIPS 2021 • Yang Zhang, Ashkan Khakzar, Yawei Li, Azade Farshad, Seong Tae Kim, Nassir Navab
We propose a method to identify features with predictive information in the input domain.
no code implementations • 18 Sep 2021 • Anastasia Makarevich, Azade Farshad, Vasileios Belagiannis, Nassir Navab
In this work, we present MetaMedSeg, a gradient-based meta-learning algorithm that redefines the meta-learning task for the volumetric medical data with the goal to capture the variety between the slices.
no code implementations • ICCV 2021 • Sarthak Garg, Helisa Dhamo, Azade Farshad, Sabrina Musatian, Nassir Navab, Federico Tombari
Scene graphs, composed of nodes as objects and directed-edges as relationships among objects, offer an alternative representation of a scene that is more semantically grounded than images.
no code implementations • 1 Jan 2021 • Azade Farshad, Samin Hamidi, Nassir Navab
Data clustering is a well-known unsupervised learning approach.
no code implementations • 17 Aug 2020 • Yousef Yeganeh, Azade Farshad, Nassir Navab, Shadi Albarqouni
Federated learning (FL) has been a promising approach in the field of medical imaging in recent years.
1 code implementation • CVPR 2020 • Helisa Dhamo, Azade Farshad, Iro Laina, Nassir Navab, Gregory D. Hager, Federico Tombari, Christian Rupprecht
In our work, we address the novel problem of image manipulation from scene graphs, in which a user can edit images by merely applying changes in the nodes or edges of a semantic graph that is generated from the image.
no code implementations • 28 Mar 2018 • Vasileios Belagiannis, Azade Farshad, Fabio Galasso
Neural network compression has recently received much attention due to the computational requirements of modern deep models.