4 citations · 12 across the 7 of their papers we have counts for
7 papers
PRISM: Progressive Restoration for Scene Graph-based Image Manipulation
Pavel Jahoda, Azade Farshad, Yousef Yeganeh +2
Scene graphs have emerged as accurate descriptive priors for image generation and manipulation tasks, however, their complexity and diversity of the shapes and relations of objects…
AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection
Mehdi Astaraki, Francesca De Benetti, Yousef Yeganeh +5
Robust and accurate detection and segmentation of heterogenous tumors appearing in different anatomical organs with supervised methods require large-scale labeled datasets covering…
SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesis
Azade Farshad, Yousef Yeganeh, Yu Chi +3
Text-conditioned image generation has made significant progress in recent years with generative adversarial networks and more recently, diffusion models. While diffusion models con…
SCOPE: Structural Continuity Preservation for Medical Image Segmentation
Yousef Yeganeh, Azade Farshad, Goktug Guevercin +5
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 method…
DIAMANT: Dual Image-Attention Map Encoders For Medical Image Segmentation
Yousef Yeganeh, Azade Farshad, Peter Weinberger +3
Although purely transformer-based architectures showed promising performance in many computer vision tasks, many hybrid models consisting of CNN and transformer blocks are introduc…
Shape-Aware Masking for Inpainting in Medical Imaging
Yousef Yeganeh, Azade Farshad, Nassir Navab
Inpainting has recently been proposed as a successful deep learning technique for unsupervised medical image model discovery. The masks used for inpainting are generally independen…