386 citations · 419 across the 8 of their papers we have counts for
9 papers
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
Hongwei Bran Li, Fernando Navarro, Ivan Ezhov +77
Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a sign…
A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data
Hans Liebl, David Schinz, Anjany Sekuboyina +13
With the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning…
Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments
Reza Nasirigerdeh, Mohammad Bakhtiari, Reihaneh Torkzadehmahani +4
Federated learning has faced performance and network communication challenges, especially in the environments where the data is not independent and identically distributed (IID) ac…
Cranial Implant Prediction using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement
Amirhossein Bayat, Suprosanna Shit, Adrian Kilian +3
Designing of a cranial implant needs a 3D understanding of the complete skull shape. Thus, taking a 2D approach is sub-optimal, since a 2D model lacks a holistic 3D view of both th…
Robustification of Segmentation Models Against Adversarial Perturbations In Medical Imaging
Hanwool Park, Amirhossein Bayat, Mohammad Sabokrou +2
This paper presents a novel yet efficient defense framework for segmentation models against adversarial attacks in medical imaging. In contrary to the defense methods against adver…
A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images
Stefan Gerl, Johannes C. Paetzold, Hailong He +7
Raster-scan optoacoustic mesoscopy (RSOM) is a powerful, non-invasive optical imaging technique for functional, anatomical, and molecular skin and tissue analysis. However, both th…
Inferring the 3D Standing Spine Posture from 2D Radiographs
Amirhossein Bayat, Anjany Sekuboyina, Johannes C. Paetzold +5
The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D. An upright spinal pose (i.e. standing) under natural…
Feedback Graph Attention Convolutional Network for Medical Image Enhancement
Xiaobin Hu, Yanyang Yan, Wenqi Ren +4
Artifacts, blur and noise are the common distortions degrading MRI images during the acquisition process, and deep neural networks have been demonstrated to help in improving image…
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat +66
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit cl…