578 citations · 2.5k across the 216 of their papers we have counts for
22 papers · 1 filter
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection
Alexander Lehner, Stefano Gasperini, Alvaro Marcos-Ramiro +5
As 3D object detection on point clouds relies on the geometrical relationships between the points, non-standard object shapes can hinder a method's detection capability. However, i…
CertainNet: Sampling-free Uncertainty Estimation for Object Detection
Stefano Gasperini, Jan Haug, Mohammad-Ali Nikouei Mahani +4
Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings. In perception for autonomous driving, measuring the uncertainty means providing…
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information
Yang Zhang, Ashkan Khakzar, Yawei Li +3
One principal approach for illuminating a black-box neural network is feature attribution, i.e. identifying the importance of input features for the network's prediction. The predi…
Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation
Mert Asim Karaoglu, Nikolas Brasch, Marijn Stollenga +4
Depth estimation from monocular images is an important task in localization and 3D reconstruction pipelines for bronchoscopic navigation. Various supervised and self-supervised dee…
Motion-Aware Robotic 3D Ultrasound
Zhongliang Jiang, Hanyu Wang, Zhenyu Li +7
Robotic three-dimensional (3D) ultrasound (US) imaging has been employed to overcome the drawbacks of traditional US examinations, such as high inter-operator variability and lack…
Multimodal Semantic Scene Graphs for Holistic Modeling of Surgical Procedures
Ege Özsoy, Evin Pınar Örnek, Ulrich Eck +2
From a computer science viewpoint, a surgical domain model needs to be a conceptual one incorporating both behavior and data. It should therefore model actors, devices, tools, thei…
Deep Direct Volume Rendering: Learning Visual Feature Mappings From Exemplary Images
Jakob Weiss, Nassir Navab
Volume Rendering is an important technique for visualizing three-dimensional scalar data grids and is commonly employed for scientific and medical image data. Direct Volume Renderi…
Rethinking Ultrasound Augmentation: A Physics-Inspired Approach
Maria Tirindelli, Christine Eilers, Walter Simson +3
Medical Ultrasound (US), despite its wide use, is characterized by artifacts and operator dependency. Those attributes hinder the gathering and utilization of US datasets for the t…
GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference
Mahsa Ghorbani, Mojtaba Bahrami, Anees Kazi +3
The increased amount of multi-modal medical data has opened the opportunities to simultaneously process various modalities such as imaging and non-imaging data to gain a comprehens…
Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models
Ashkan Khakzar, Sabrina Musatian, Jonas Buchberger +5
Convolutional neural networks are showing promise in the automatic diagnosis of thoracic pathologies on chest x-rays. Their black-box nature has sparked many recent works to explai…
Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features
Ashkan Khakzar, Yang Zhang, Wejdene Mansour +5
Neural networks have demonstrated remarkable performance in classification and regression tasks on chest X-rays. In order to establish trust in the clinical routine, the networks'…
Neural Response Interpretation through the Lens of Critical Pathways
Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja +3
Is critical input information encoded in specific sparse pathways within the neural network? In this work, we discuss the problem of identifying these critical pathways and subsequ…
SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences
Shun-Cheng Wu, Johanna Wald, Keisuke Tateno +2
Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks. This work proposes a method to incrementally build up semanti…
IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction
Anees Kazi, Soroush Farghadani, Nassir Navab
Interpretability in Graph Convolutional Networks (GCNs) has been explored to some extent in computer vision in general, yet, in the medical domain, it requires further examination.…
Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning
Christina Bukas, Bailiang Jian, Luis F. Rodriguez Venegas +9
Symptomatic spinal vertebral compression fractures (VCFs) often require osteoplasty treatment. A cement-like material is injected into the bone to stabilize the fracture, restore t…
GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images
Aadhithya Sankar, Matthias Keicher, Rami Eisawy +4
Disentangled representations can be useful in many downstream tasks, help to make deep learning models more interpretable, and allow for control over features of synthetically gene…
Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias
Ario Sadafi, Lucía María Moya Sans, Asya Makhro +5
Hereditary hemolytic anemias are genetic disorders that affect the shape and density of red blood cells. Genetic tests currently used to diagnose such anemias are expensive and una…
Co-Generation and Segmentation for Generalized Surgical Instrument Segmentation on Unlabelled Data
Megha Kalia, Tajwar Abrar Aleef, Nassir Navab +1
Surgical instrument segmentation for robot-assisted surgery is needed for accurate instrument tracking and augmented reality overlays. Therefore, the topic has been the subject of…
OperA: Attention-Regularized Transformers for Surgical Phase Recognition
Tobias Czempiel, Magdalini Paschali, Daniel Ostler +3
In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences. A novel attention regularization loss encourages the…
Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs
Seong Tae Kim, Leili Goli, Magdalini Paschali +7
Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity a…
Semi-Supervised Federated Peer Learning for Skin Lesion Classification
Tariq Bdair, Nassir Navab, Shadi Albarqouni
Globally, Skin carcinoma is among the most lethal diseases. Millions of people are diagnosed with this cancer every year. Sill, early detection can decrease the medication cost and…
RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data
Mahsa Ghorbani, Anees Kazi, Mahdieh Soleymani Baghshah +2
Disease prediction is a well-known classification problem in medical applications. GCNs provide a powerful tool for analyzing the patients' features relative to each other. This ca…