no code implementations • 30 Dec 2024 • Yousef Yeganeh, Ioannis Charisiadis, Marta Hasny, Martin Hartenberger, Björn Ommer, Nassir Navab, Azade Farshad, Ehsan Adeli
Scaling by training on large datasets has been shown to enhance the quality and fidelity of image generation and manipulation with diffusion models; however, such large datasets are not always accessible in medical imaging due to cost and privacy issues, which contradicts one of the main applications of such models to produce synthetic samples where real data is scarce.
no code implementations • 29 Dec 2024 • Yousef Yeganeh, Rui Xiao, Goktug Guvercin, Nassir Navab, Azade Farshad
While conventional computer vision emphasizes pixel-level and feature-based objectives, medical image analysis of intricate biological structures necessitates explicit representation of their complex topological properties.
no code implementations • 3 Nov 2024 • Asra Aslam, Sachini Herath, Ziqi Huang, Estefania Talavera, Deblina Bhattacharjee, Himangi Mittal, Vanessa Staderini, Mengwei Ren, Azade Farshad
In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2024, organized alongside the CVPR 2024 in Seattle, Washington, United States.
no code implementations • 23 Oct 2024 • Yousef Yeganeh, Rachmadio Lazuardi, Amir Shamseddin, Emine Dari, Yash Thirani, Nassir Navab, Azade Farshad
The results of our experiments demonstrate high-fidelity video generation for laparoscopy procedures, which enables various applications in SDS.
1 code implementation • 20 Sep 2024 • Sven Lüpke, Yousef Yeganeh, Ehsan Adeli, Nassir Navab, Azade Farshad
Our approach utilizes latent diffusion models and a two-step generative process: first, unobserved physical tissue property maps are synthesized using a latent diffusion model, and then these maps are combined with a physical signal model to generate the final MRI scan.
no code implementations • 29 Jul 2024 • Çağhan Köksal, Ghazal Ghazaei, Felix Holm, Azade Farshad, Nassir Navab
Graph-based holistic scene representations facilitate surgical workflow understanding and have recently demonstrated significant success.
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 • 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 • 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, 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.