activity
20192024
most citedImage to Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography

110 citations · 116 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2024

Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis

Yousef Yeganeh, Azade Farshad, Ioannis Charisiadis +5

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 ar…

eess.IV2024★ 1 cited

Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis

Sven Lüpke, Yousef Yeganeh, Ehsan Adeli +2

Recent advances in generative models for medical imaging have shown promise in representing multiple modalities. However, the variability in modality availability across datasets l…

cs.LG2023

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…

cs.CV2023★ 3 cited

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…

cs.CV2023★ 2 cited

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…

cs.LG2022

Perception-Aligned AI Outputs: End-to-End Visual Prediction for Uncertainty Communication in Clinical Decision-Making

Mohammad Eslami, Solale Tabarestani, Saber Kazeminasab +9

Explainable Artificial Intelligence (XAI) is essential for trustworthy AI in healthcare, yet many existing methods rely on technical explanations that are difficult for clinicians…