4 citations · 15 across the 18 of their papers we have counts for
19 papers
INTERACT-CMIL: Multi-Task Shared Learning and Inter-Task Consistency for Conjunctival Melanocytic Intraepithelial Lesion Grading
Mert Ikinci, Luna Toma, Karin U. Loeffler +6
Accurate grading of Conjunctival Melanocytic Intraepithelial Lesions (CMIL) is essential for treatment and melanoma prediction but remains difficult due to subtle morphological cue…
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…
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…
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…
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…
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…
Inverse Distance Aggregation for Federated Learning with Non-IID Data
Yousef Yeganeh, Azade Farshad, Nassir Navab +1
Federated learning (FL) has been a promising approach in the field of medical imaging in recent years. A critical problem in FL, specifically in medical scenarios is to have a more…