Florian Kofler | DeepAI

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∙ 07/31/2023

Framing image registration as a landmark detection problem for better representation of clinical relevance

Nowadays, registration methods are typically evaluated based on sub-reso...

0 Diana Waldmannstetter, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 12/31/2022

Approaching Peak Ground Truth

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

0 Florian Kofler, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 04/05/2022

A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation

Deep learning models for medical image segmentation can fail unexpectedl...

15 Lucas Fidon, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 09/03/2021

Semi-Implicit Neural Solver for Time-dependent Partial Differential Equations

Fast and accurate solutions of time-dependent partial differential equat...

4 Suprosanna, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 04/22/2020

Red-GAN: Attacking class imbalance via conditioned generation. Yet another medical imaging perspective

Exploiting learning algorithms under scarce data regimes is a limitation...

8 Ahmad B Qasim, et al. ∙

∙ 07/01/2019

Neural parameters estimation for brain tumor growth modeling

Understanding the dynamics of brain tumor progression is essential for o...

4 Ivan Ezhov, et al. ∙

∙ 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. ∙

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