19 citations · 41 across the 24 of their papers we have counts for
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A Continual Learning Approach for Cross-Domain White Blood Cell Classification
Ario Sadafi, Raheleh Salehi, Armin Gruber +4
Accurate classification of white blood cells in peripheral blood is essential for diagnosing hematological diseases. Due to constantly evolving clinical settings, data sources, and…
A Study of Age and Sex Bias in Multiple Instance Learning based Classification of Acute Myeloid Leukemia Subtypes
Ario Sadafi, Matthias Hehr, Nassir Navab +1
Accurate classification of Acute Myeloid Leukemia (AML) subtypes is crucial for clinical decision-making and patient care. In this study, we investigate the potential presence of a…
Can ultrasound confidence maps predict sonographers' labeling variability?
Vanessa Gonzalez Duque, Leonhard Zirus, Yordanka Velikova +2
Measuring cross-sectional areas in ultrasound images is a standard tool to evaluate disease progress or treatment response. Often addressed today with supervised deep-learning segm…
DefCor-Net: Physics-Aware Ultrasound Deformation Correction
Zhongliang Jiang, Yue Zhou, Dongliang Cao +1
The recovery of morphologically accurate anatomical images from deformed ones is challenging in ultrasound (US) image acquisition, but crucial to accurate and consistent diagnosis,…
LOTUS: Learning to Optimize Task-based US representations
Yordanka Velikova, Mohammad Farid Azampour, Walter Simson +2
Anatomical segmentation of organs in ultrasound images is essential to many clinical applications, particularly for diagnosis and monitoring. Existing deep neural networks require…
Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph
Zhongliang Jiang, Chenyang Li, Xuesong Li +1
Autonomous ultrasound (US) imaging has gained increased interest recently, and it has been seen as a potential solution to overcome the limitations of free-hand US examinations, su…
Motion Magnification in Robotic Sonography: Enabling Pulsation-Aware Artery Segmentation
Dianye Huang, Yuan Bi, Nassir Navab +1
Ultrasound (US) imaging is widely used for diagnosing and monitoring arterial diseases, mainly due to the advantages of being non-invasive, radiation-free, and real-time. In order…
Rad-ReStruct: A Novel VQA Benchmark and Method for Structured Radiology Reporting
Chantal Pellegrini, Matthias Keicher, Ege Özsoy +1
Radiology reporting is a crucial part of the communication between radiologists and other medical professionals, but it can be time-consuming and error-prone. One approach to allev…
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…
Self-Supervised Learning for Physiologically-Based Pharmacokinetic Modeling in Dynamic PET
Francesca De Benetti, Walter Simson, Magdalini Paschali +5
Dynamic positron emission tomography imaging (dPET) provides temporally resolved images of a tracer enabling a quantitative measure of physiological processes. Voxel-wise physiolog…
DopUS-Net: Quality-Aware Robotic Ultrasound Imaging based on Doppler Signal
Zhongliang Jiang, Felix Duelmer, Nassir Navab
Medical ultrasound (US) is widely used to evaluate and stage vascular diseases, in particular for the preliminary screening program, due to the advantage of being radiation-free. H…
Skeleton Graph-based Ultrasound-CT Non-rigid Registration
Zhongliang Jiang, Xuesong Li, Chenyu Zhang +3
Autonomous ultrasound (US) scanning has attracted increased attention, and it has been seen as a potential solution to overcome the limitations of conventional US examinations, suc…
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…
Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images
Ario Sadafi, Oleksandra Adonkina, Ashkan Khakzar +5
Explainability is a key requirement for computer-aided diagnosis systems in clinical decision-making. Multiple instance learning with attention pooling provides instance-level expl…
Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays
Yiheng Xiong, Jingsong Liu, Kamilia Zaripova +3
The extraction of structured clinical information from free-text radiology reports in the form of radiology graphs has been demonstrated to be a valuable approach for evaluating th…
MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization
Yuan Bi, Zhongliang Jiang, Ricarda Clarenbach +3
Generalization capabilities of learning-based medical image segmentation across domains are currently limited by the performance degradation caused by the domain shift, particularl…