61 citations · 334 across the 42 of their papers we have counts for
27 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
Quantifying Explainers of Graph Neural Networks in Computational Pathology
Guillaume Jaume, Pushpak Pati, Behzad Bozorgtabar +7
Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniqu…
Modality Attention and Sampling Enables Deep Learning with Heterogeneous Marker Combinations in Fluorescence Microscopy
Alvaro Gomariz, Tiziano Portenier, Patrick M. Helbling +4
Fluorescence microscopy allows for a detailed inspection of cells, cellular networks, and anatomical landmarks by staining with a variety of carefully-selected markers visualized a…
HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification
Pushpak Pati, Guillaume Jaume, Lauren Alisha Fernandes +13
Cancer diagnosis, prognosis, and therapeutic response prediction are heavily influenced by the relationship between the histopathological structures and the function of the tissue.…
Towards Explainable Graph Representations in Digital Pathology
Guillaume Jaume, Pushpak Pati, Antonio Foncubierta-Rodriguez +6
Explainability of machine learning (ML) techniques in digital pathology (DP) is of great significance to facilitate their wide adoption in clinics. Recently, graph techniques encod…
GramGAN: Deep 3D Texture Synthesis From 2D Exemplars
Tiziano Portenier, Siavash Bigdeli, Orcun Goksel
We present a novel texture synthesis framework, enabling the generation of infinite, high-quality 3D textures given a 2D exemplar image. Inspired by recent advances in natural text…
Deep Image Translation for Enhancing Simulated Ultrasound Images
Lin Zhang, Tiziano Portenier, Christoph Paulus +1
Ultrasound simulation based on ray tracing enables the synthesis of highly realistic images. It can provide an interactive environment for training sonographers as an educational t…
Deep Network for Scatterer Distribution Estimation for Ultrasound Image Simulation
Lin Zhang, Valery Vishnevskiy, Orcun Goksel
Simulation-based ultrasound training can be an essential educational tool. Realistic ultrasound image appearance with typical speckle texture can be modeled as convolution of a poi…
Delineating Bone Surfaces in B-Mode Images Constrained by Physics of Ultrasound Propagation
Firat Ozdemir, Christine Tanner, Orcun Goksel
Bone surface delineation in ultrasound is of interest due to its potential in diagnosis, surgical planning, and post-operative follow-up in orthopedics, as well as the potential of…
Active Learning for Segmentation Based on Bayesian Sample Queries
Firat Ozdemir, Zixuan Peng, Philipp Fuernstahl +2
Segmentation of anatomical structures is a fundamental image analysis task for many applications in the medical field. Deep learning methods have been shown to perform well, but fo…
Weighted Mean Curvature
Yuanhao Gong, Orcun Goksel
In image processing tasks, spatial priors are essential for robust computations, regularization, algorithmic design and Bayesian inference. In this paper, we introduce weighted mea…
Siamese Networks with Location Prior for Landmark Tracking in Liver Ultrasound Sequences
Alvaro Gomariz, Weiye Li, Ece Ozkan +2
Image-guided radiation therapy can benefit from accurate motion tracking by ultrasound imaging, in order to minimize treatment margins and radiate moving anatomical targets, e.g.,…
Implicit Modeling with Uncertainty Estimation for Intravoxel Incoherent Motion Imaging
Lin Zhang, Valery Vishnevskiy, Andras Jakab +1
Intravoxel incoherent motion (IVIM) imaging allows contrast-agent free in vivo perfusion quantification with magnetic resonance imaging (MRI). However, its use is limited by typica…