7 citations · 23 across the 27 of their papers we have counts for
30 papers
Predicting Postprandial Glycemic Response from Meal Images, Clinical Variables, and Gut Microbiome Information
Varvara Kondratyeva, Kamilia Zaripova, Nassir Navab +1
Predicting postprandial glycemic response (PPGR) is fundamental to personalized nutrition and type 2 diabetes management, yet existing approaches typically rely on manually reporte…
Millimeter-wave Imaging for Anthropometric Body Measurement
Miriam Senne, Benjamin D. Killeen, Christoph Baur +2
Body shape and circumferences are clinically informative biomarkers for risk stratification, including measures such as waist to hip ratio, limb and trunk girths, yet conventional…
Knowledge Graph Sparsification for GNN-based Rare Disease Diagnosis
Premt Cara, Kamilia Zaripova, David Bani-Harouni +2
Rare genetic disease diagnosis faces critical challenges: insufficient patient data, inaccessible full genome sequencing, and the immense number of possible causative genes. These…
DeepAf: One-Shot Spatiospectral Auto-Focus Model for Digital Pathology
Yousef Yeganeh, Maximilian Frantzen, Michael Lee +3
While Whole Slide Imaging (WSI) scanners remain the gold standard for digitizing pathology samples, their high cost limits accessibility in many healthcare settings. Other low-cost…
Stress-Aware Resilient Neural Training
Ashkan Shakarami, Yousef Yeganeh, Azade Farshad +3
This paper introduces Stress-Aware Learning, a resilient neural training paradigm in which deep neural networks dynamically adjust their optimization behavior - whether under stabl…
Unit-Based Histopathology Tissue Segmentation via Multi-Level Feature Representation
Ashkan Shakarami, Azade Farshad, Yousef Yeganeh +4
We propose UTS, a unit-based tissue segmentation framework for histopathology that classifies each fixed-size 32 * 32 tile, rather than each pixel, as the segmentation unit. This a…
HieraSurg: Hierarchy-Aware Diffusion Model for Surgical Video Generation
Diego Biagini, Nassir Navab, Azade Farshad
Surgical Video Synthesis has emerged as a promising research direction following the success of diffusion models in general-domain video generation. Although existing approaches ac…
PhenoKG: Knowledge Graph-Driven Gene Discovery and Patient Insights from Phenotypes Alone
Kamilia Zaripova, Ege Özsoy, Nassir Navab +1
Identifying causative genes from patient phenotypes remains a significant challenge in precision medicine, with important implications for the diagnosis and treatment of genetic di…
VeLU: Variance-enhanced Learning Unit for Deep Neural Networks
Ashkan Shakarami, Yousef Yeganeh, Azade Farshad +3
Activation functions play a critical role in deep neural networks by shaping gradient flow, optimization stability, and generalization. While ReLU remains widely used due to its si…
Conformable Convolution for Topologically Aware Learning of Complex Anatomical Structures
Yousef Yeganeh, Rui Xiao, Goktug Guvercin +2
While conventional computer vision emphasizes pixel-level and feature-based objectives, medical image analysis of intricate biological structures necessitates explicit representati…
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…
WiCV@CVPR2024: The Thirteenth Women In Computer Vision Workshop at the Annual CVPR Conference
Asra Aslam, Sachini Herath, Ziqi Huang +6
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. WiCV aims to amplif…
VISAGE: Video Synthesis using Action Graphs for Surgery
Yousef Yeganeh, Rachmadio Lazuardi, Amir Shamseddin +4
Surgical data science (SDS) is a field that analyzes patient data before, during, and after surgery to improve surgical outcomes and skills. However, surgical data is scarce, heter…
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…
SANGRIA: Surgical Video Scene Graph Optimization for Surgical Workflow Prediction
Çağhan Köksal, Ghazal Ghazaei, Felix Holm +2
Graph-based holistic scene representations facilitate surgical workflow understanding and have recently demonstrated significant success. However, this task is often hindered by th…
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…
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…
DisPositioNet: Disentangled Pose and Identity in Semantic Image Manipulation
Azade Farshad, Yousef Yeganeh, Helisa Dhamo +2
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…
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…
Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data
Yousef Yeganeh, Azade Farshad, Johann Boschmann +3
Federated learning (FL) is a distributed learning method that offers medical institutes the prospect of collaboration in a global model while preserving the privacy of their patien…
Y-Net: A Spatiospectral Dual-Encoder Networkfor Medical Image Segmentation
Azade Farshad, Yousef Yeganeh, Peter Gehlbach +1
Automated segmentation of retinal optical coherence tomography (OCT) images has become an important recent direction in machine learning for medical applications. We hypothesize th…
MIGS: Meta Image Generation from Scene Graphs
Azade Farshad, Sabrina Musatian, Helisa Dhamo +1
Generation of images from scene graphs is a promising direction towards explicit scene generation and manipulation. However, the images generated from the scene graphs lack quality…