Benedikt Wiestler

research

∙ 05/30/2023

The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)

Gliomas are the most common type of primary brain tumors. Although gliom...

0 Maruf Adewole, et al. ∙

research

∙ 05/26/2023

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Pediatric tumors of the central nervous system are the most common cause...

0 Anahita Fathi Kazerooni, et al. ∙

research

∙ 05/15/2023

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Automated brain tumor segmentation methods are well established, reachin...

0 Hongwei Bran Li, et al. ∙

research

∙ 05/15/2023

The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting

A myriad of algorithms for the automatic analysis of brain MR images is ...

0 Florian Kofler, et al. ∙

research

∙ 05/12/2023

The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma

Meningiomas are the most common primary intracranial tumor in adults and...

0 Dominic LaBella, et al. ∙

research

∙ 04/04/2023

Primitive Simultaneous Optimization of Similarity Metrics for Image Registration

Even though simultaneous optimization of similarity metrics represents a...

0 Diana Waldmannstetter, et al. ∙

research

∙ 03/27/2023

Multi-contrast MRI Super-resolution via Implicit Neural Representations

Clinical routine and retrospective cohorts commonly include multi-parame...

0 Julian McGinnis, et al. ∙

research

∙ 03/21/2023

Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading

Vertebral fractures are a consequence of osteoporosis, with significant ...

0 Matthias Keicher, et al. ∙

research

∙ 03/15/2023

Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection

Early and accurate disease detection is crucial for patient management a...

0 Cosmin I. Bercea, et al. ∙

research

∙ 01/18/2023

ViT-AE++: Improving Vision Transformer Autoencoder for Self-supervised Medical Image Representations

Self-supervised learning has attracted increasing attention as it learns...

0 Chinmay Prabhakar, et al. ∙

research

∙ 12/31/2022

Approaching Peak Ground Truth

Machine learning models are typically evaluated by computing similarity ...

0 Florian Kofler, et al. ∙

research

∙ 12/03/2022

A Domain-specific Perceptual Metric via Contrastive Self-supervised Representation: Applications on Natural and Medical Images

Quantifying the perceptual similarity of two images is a long-standing p...

0 Hongwei Bran Li, et al. ∙

research

∙ 06/14/2022

ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset

Magnetic resonance imaging (MRI) is a central modality for stroke imagin...

15 Moritz Roman Hernandez Petzsche, et al. ∙

research

∙ 05/17/2022

blob loss: instance imbalance aware loss functions for semantic segmentation

Deep convolutional neural networks have proven to be remarkably effectiv...

16 Florian Kofler, et al. ∙

research

∙ 05/09/2022

A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling

Solving the inverse problem is the key step in evaluating the capacity o...

0 Ivan Ezhov, et al. ∙

research

∙ 03/30/2022

Interpretable Vertebral Fracture Diagnosis

Do black-box neural network models learn clinically relevant features fo...

0 Paul Engstler, et al. ∙

research

∙ 02/08/2022

On the Pitfalls of Using the Residual Error as Anomaly Score

Many current state-of-the-art methods for anomaly localization in medica...

0 Felix Meissen, et al. ∙

research

∙ 12/13/2021

The Brain Tumor Sequence Registration Challenge: Establishing Correspondence between Pre-Operative and Follow-up MRI scans of diffuse glioma patients

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scan...

0 Bhakti Baheti, et al. ∙

research

∙ 11/16/2021

FedCostWAvg: A new averaging for better Federated Learning

We propose a simple new aggregation strategy for federated learning that...

5 Leon Mächler, et al. ∙

research

∙ 11/07/2021

Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling

Current treatment planning of patients diagnosed with brain tumor could ...

35 Ivan Ezhov, et al. ∙

research

∙ 07/05/2021

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

The BraTS 2021 challenge celebrates its 10th anniversary and is jointly ...

0 Ujjwal Baid, et al. ∙

research

∙ 03/10/2021

Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient

In this study, we explore quantitative correlates of qualitative human e...

29 Florian Kofler, et al. ∙

research

∙ 03/06/2021

Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations

Radiomic representations can quantify properties of regions of interest ...

19 Hongwei Li, et al. ∙

research

∙ 03/05/2021

FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation

In recent years, data-driven machine learning (ML) methods have revoluti...

15 Cosmin I. Bercea, et al. ∙

research

∙ 09/09/2020

Real-time Bayesian personalization via a learnable brain tumor growth model

Modeling of brain tumor dynamics has the potential to advance therapeuti...

1 Ivan Ezhov, et al. ∙

research

∙ 06/23/2020

Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI

Brain pathologies can vary greatly in size and shape, ranging from few p...

0 Christoph Baur, et al. ∙

research

∙ 04/07/2020

Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study

Deep unsupervised representation learning has recently led to new approa...

0 Christoph Baur, et al. ∙

research

∙ 03/18/2020

Train, Learn, Expand, Repeat

High-quality labeled data is essential to successfully train supervised ...

0 Abhijeet Parida, et al. ∙

research

∙ 01/25/2020

e-UDA: Efficient Unsupervised Domain Adaptation for Cross-Site Medical Image Segmentation

Domain adaptation in healthcare data is a potentially critical component...

17 Hongwei Li, et al. ∙

research

∙ 04/29/2019

DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis

Recent studies on medical image synthesis reported promising results usi...

0 Hongwei Li, et al. ∙

research

∙ 11/05/2018

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different d...

0 Spyridon Bakas, et al. ∙

research

∙ 07/02/2018

Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans and Bayesian Inference

Glioblastoma is a highly invasive brain tumor, whose cells infiltrate su...

0 Jana Lipkova, et al. ∙

research

∙ 07/02/2018

Personalized Radiotherapy Design for Glioblastoma Using Mathematical Models, Multimodal Scans and Bayesian Inference

Glioblastoma is a highly invasive brain tumor, whose cells infiltrate su...

0 Jana Lipkova, et al. ∙

research

∙ 07/02/2018

Personalized Radiotherapy Planning for Glioma Using Multimodal Bayesian Model Calibration

Existing radiotherapy (RT) plans for brain tumors derive from population...

0 Jana Lipkova, et al. ∙

research

∙ 04/12/2018

Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images

Reliably modeling normality and differentiating abnormal appearances fro...

0 Christoph Baur, et al. ∙

Benedikt Wiestler

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