61 citations · 334 across the 42 of their papers we have counts for
50 papers
Fast Alternating Radial Beamforming for Speed-of-Sound Imaging Based on Apparent Speckle Shifts
Dieter Schweizer, Can Deniz Bezek, Orcun Goksel
Pulse-echo speed-of-sound (SoS) imaging based on minute misalignments between consecutively acquired ultrasound images traditionally relies on images beamformed on Cartesian grids.…
Uncertainty Estimation for Trust Attribution to Speed-of-Sound Reconstruction with Variational Networks
Sonia Laguna, Lin Zhang, Can Deniz Bezek +4
Speed-of-sound (SoS) is a biomechanical characteristic of tissue, and its imaging can provide a promising biomarker for diagnosis. Reconstructing SoS images from ultrasound acquisi…
Image-Based Metrics in Ultrasound for Estimation of Global Speed-of-Sound
Roman Denkin, Orcun Goksel
Accurate speed-of-sound (SoS) estimation is crucial for ultrasound image formation, yet conventional systems often rely on an assumed value for imaging. We propose to leverage conv…
FGGP: Fixed-Rate Gradient-First Gradual Pruning
Lingkai Zhu, Can Deniz Bezek, Orcun Goksel
In recent years, the increasing size of deep learning models and their growing demand for computational resources have drawn significant attention to the practice of pruning neural…
Learning the Imaging Model of Speed-of-Sound Reconstruction via a Convolutional Formulation
Can Deniz Bezek, Maxim Haas, Richard Rau +1
Speed-of-sound (SoS) is an emerging ultrasound contrast modality, where pulse-echo techniques using conventional transducers offer multiple benefits. For estimating tissue SoS dist…
Robust Imaging of Speed-of-Sound Using Virtual Source Transmission
Dieter Schweizer, Richard Rau, Can Deniz Bezek +2
Speed-of-sound (SoS) is a novel imaging biomarker for assessing biomechanical characteristics of soft tissues. SoS imaging in pulse-echo mode using conventional ultrasound systems…
Unpaired Translation from Semantic Label Maps to Images by Leveraging Domain-Specific Simulations
Lin Zhang, Tiziano Portenier, Orcun Goksel
Photorealistic image generation from simulated label maps are necessitated in several contexts, such as for medical training in virtual reality. With conventional deep learning met…
Generative appearance replay for continual unsupervised domain adaptation
Boqi Chen, Kevin Thandiackal, Pushpak Pati +1
Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may beco…
Multi-scale Feature Alignment for Continual Learning of Unlabeled Domains
Kevin Thandiackal, Luigi Piccinelli, Pushpak Pati +1
Methods for unsupervised domain adaptation (UDA) help to improve the performance of deep neural networks on unseen domains without any labeled data. Especially in medical disciplin…
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer
Pushpak Pati, Guillaume Jaume, Zeineb Ayadi +4
The segmentation and automatic identification of histological regions of diagnostic interest offer a valuable aid to pathologists. However, segmentation methods are hampered by the…
Analytical Estimation of Beamforming Speed-of-Sound Using Transmission Geometry
Can Deniz Bezek, Orcun Goksel
Most ultrasound imaging techniques necessitate the fundamental step of converting temporal signals received from transducer elements into a spatial echogenecity map. This beamformi…
HDRfeat: A Feature-Rich Network for High Dynamic Range Image Reconstruction
Lingkai Zhu, Fei Zhou, Bozhi Liu +1
A major challenge for high dynamic range (HDR) image reconstruction from multi-exposed low dynamic range (LDR) images, especially with dynamic scenes, is the extraction and merging…
Global Speed-of-Sound Prediction Using Transmission Geometry
Can Deniz Bezek, Mert Bilgin, Lin Zhang +1
Most ultrasound (US) imaging techniques use spatially-constant speed-of-sound (SoS) values for beamforming. Having a discrepancy between the actual and used SoS value leads to aber…
Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images
Kevin Thandiackal, Boqi Chen, Pushpak Pati +4
Multiple Instance Learning (MIL) methods have become increasingly popular for classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology. Most MIL methods operate…
Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation
Alvaro Gomariz, Huanxiang Lu, Yun Yvonna Li +7
Accurate segmentation of retinal fluids in 3D Optical Coherence Tomography images is key for diagnosis and personalized treatment of eye diseases. While deep learning has been succ…
Estimating Mean Speed-of-Sound from Sequence-Dependent Geometric Disparities
Xenia Augustin, Lin Zhang, Orcun Goksel
In ultrasound beamforming, focusing time delays are typically computed with a spatially constant speed-of-sound (SoS) assumption. A mismatch between beamforming and true medium SoS…
Phase-Aberration Correction in Shear-wave Elastography Imaging Using Local Speed-of-Sound Adaptive Beamforming
Bhaskara Rao Chintada, Richard Rau, Orcun Goksel
Shear-wave Elastography Imaging (SWEI) is a noninvasive imaging modality that provides tissue elasticity information by measuring the travelling speed of an induced shear-wave. It…
Generative Feature-driven Image Replay for Continual Learning
Kevin Thandiackal, Tiziano Portenier, Andrea Giovannini +2
Neural networks are prone to catastrophic forgetting when trained incrementally on different tasks. Popular incremental learning methods mitigate such forgetting by retaining a sub…
Motion Estimation for Optical Coherence Elastography Using Signal Phase and Intensity
Hossein Khodadadi, Orcun Goksel, Sabine Kling
Displacement estimation in optical coherence tomography (OCT) imaging is relevant for several potential applications, e.g. for optical coherence elastography (OCE) for corneal biom…
Hierarchical Graph Representations in Digital Pathology
Pushpak Pati, Guillaume Jaume, Antonio Foncubierta +14
Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entitie…
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs
Valentin Anklin, Pushpak Pati, Guillaume Jaume +6
Segmenting histology images into diagnostically relevant regions is imperative to support timely and reliable decisions by pathologists. To this end, computer-aided techniques have…
Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images
Devavrat Tomar, Lin Zhang, Tiziano Portenier +1
Interactive simulation of ultrasound imaging greatly facilitates sonography training. Although ray-tracing based methods have shown promising results, obtaining realistic images re…
Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps
Alvaro Gomariz, Tiziano Portenier, César Nombela-Arrieta +1
3D microscopy is key in the investigation of diverse biological systems, and the ever increasing availability of large datasets demands automatic cell identification methods that n…
Utilizing Uncertainty Estimation in Deep Learning Segmentation of Fluorescence Microscopy Images with Missing Markers
Alvaro Gomariz, Raphael Egli, Tiziano Portenier +2
Fluorescence microscopy images contain several channels, each indicating a marker staining the sample. Since many different marker combinations are utilized in practice, it has bee…