204 citations · 381 across the 42 of their papers we have counts for
12 papers · 1 filter
CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation Models
Fabian Hörst, Moritz Rempe, Helmut Becker +3
Digital Pathology is a cornerstone in the diagnosis and treatment of diseases. A key task in this field is the identification and segmentation of cells in hematoxylin and eosin-sta…
Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation
André Ferreira, Tiago Jesus, Behrus Puladi +3
This paper presents the winning solution of task 1 and the third-placed solution of task 3 of the BraTS challenge. The use of automated tools in clinical practice has increased due…
Brain Tumour Removing and Missing Modality Generation using 3D WDM
André Ferreira, Gijs Luijten, Behrus Puladi +3
This paper presents the second-placed solution for task 8 and the participation solution for task 7 of BraTS 2024. The adoption of automated brain analysis algorithms to support cl…
Tumor likelihood estimation on MRI prostate data by utilizing k-Space information
M. Rempe, F. Hörst, C. Seibold +7
We present a novel preprocessing and prediction pipeline for the classification of magnetic resonance imaging (MRI) that takes advantage of the information rich complex valued k-Sp…
Deep Learning-based Point Cloud Registration for Augmented Reality-guided Surgery
Maximilian Weber, Daniel Wild, Jens Kleesiek +2
Point cloud registration aligns 3D point clouds using spatial transformations. It is an important task in computer vision, with applications in areas such as augmented reality (AR)…
Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images
Johannes Raufeisen, Kunpeng Xie, Fabian Hörst +9
Background: Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries
Frederic Jonske, Moon Kim, Enrico Nasca +8
It is an open secret that ImageNet is treated as the panacea of pretraining. Particularly in medical machine learning, models not trained from scratch are often finetuned based on…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
Jianning Li, Jana Fragemann, Seyed-Ahmad Ahmadi +2
The reconstruction loss and the Kullback-Leibler divergence (KLD) loss in a variational autoencoder (VAE) often play antagonistic roles, and tuning the weight of the KLD loss in $β…
FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy
André Ferreira, Jianning Li, Kelsey L. Pomykala +3
With the massive proliferation of data-driven algorithms, such as deep learning-based approaches, the availability of high-quality data is of great interest. Volumetric data is ver…
Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model
Jianning Li, David G. Ellis, Antonio Pepe +4
Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on…