Fabian Isensee

research

∙ 09/14/2023

RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

Despite the remarkable success of deep learning systems over the last de...

0 Gregor Koehler, et al. ∙

research

∙ 09/07/2023

Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection

Data augmentation (DA) is a key factor in medical image analysis, such a...

0 Balint Kovacs, et al. ∙

research

∙ 07/05/2023

Exploring new ways: Enforcing representational dissimilarity to learn new features and reduce error consistency

Independently trained machine learning models tend to learn similar feat...

0 Tassilo Wald, et al. ∙

research

∙ 04/09/2023

Transformer Utilization in Medical Image Segmentation Networks

Owing to success in the data-rich domain of natural images, Transformers...

0 Saikat Roy, et al. ∙

research

∙ 03/25/2023

MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation

The medical imaging community generates a wealth of datasets, many of wh...

0 Constantin Ulrich, et al. ∙

research

∙ 03/17/2023

MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation

There has been exploding interest in embracing Transformer-based archite...

0 Saikat Roy, et al. ∙

research

∙ 01/30/2023

[Work in progress] Scalable, out-of-the box segmentation of individual particles from mineral samples acquired with micro CT

Minerals are indispensable for a functioning modern society. Yet, their ...

0 Karol Gotkowski, et al. ∙

research

∙ 01/05/2023

CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization

Unsupervised anomaly detection in medical imaging aims to detect and loc...

0 Carsten T. Lüth, et al. ∙

research

∙ 12/03/2022

Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads

With the rise of AI in recent years and the increase in complexity of th...

0 René Caspart, et al. ∙

research

∙ 09/26/2022

Evaluation of Medical Image Segmentation Models for Uncertain, Small or Empty Reference Annotations

Performance metrics for medical image segmentation models are used to me...

0 Sophie Ostmeier, et al. ∙

research

∙ 08/23/2022

Extending nnU-Net is all you need

Semantic segmentation is one of the most popular research areas in medic...

1 Fabian Isensee, et al. ∙

research

∙ 12/16/2021

Classification of diffraction patterns using a convolutional neural network in single particle imaging experiments performed at X-ray free-electron lasers

Single particle imaging (SPI) at X-ray free electron lasers (XFELs) is p...

14 Dameli Assalauova, et al. ∙

research

∙ 06/23/2021

Continuous-Time Deep Glioma Growth Models

The ability to estimate how a tumor might evolve in the future could hav...

0 Jens Petersen, et al. ∙

research

∙ 06/09/2021

GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data

Neural Processes (NPs) are a family of conditional generative models tha...

0 Jens Petersen, et al. ∙

research

∙ 06/01/2021

nnDetection: A Self-configuring Method for Medical Object Detection

Simultaneous localisation and categorization of objects in medical image...

11 Michael Baumgartner, et al. ∙

research

∙ 04/12/2021

Common Limitations of Image Processing Metrics: A Picture Story

While the importance of automatic image analysis is increasing at an eno...

6 Annika Reinke, 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

∙ 11/15/2020

Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI

Cardiac magnetic resonance imaging (cMRI) is an integral part of diagnos...

0 Peter M. Full, et al. ∙

research

∙ 11/02/2020

nnU-Net for Brain Tumor Segmentation

We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. T...

0 Fabian Isensee, et al. ∙

research

∙ 04/27/2020

OR-UNet: an Optimized Robust Residual U-Net for Instrument Segmentation in Endoscopic Images

Segmentation of endoscopic images is an essential processing step for co...

0 Fabian Isensee, et al. ∙

research

∙ 03/23/2020

Robust Medical Instrument Segmentation Challenge 2019

Intraoperative tracking of laparoscopic instruments is often a prerequis...

13 Tobias Ross, et al. ∙

research

∙ 01/17/2020

CHAOS Challenge – Combined (CT-MR) Healthy Abdominal Organ Segmentation

Segmentation of abdominal organs has been a comprehensive, yet unresolve...

0 A. Emre Kavur, et al. ∙

research

∙ 12/02/2019

The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge

There is a large body of literature linking anatomic and geometric chara...

0 Nicholas Heller, et al. ∙

research

∙ 11/26/2019

ModelHub.AI: Dissemination Platform for Deep Learning Models

Recent advances in artificial intelligence research have led to a profus...

0 Ahmed Hosny, et al. ∙

research

∙ 08/06/2019

An attempt at beating the 3D U-Net

The U-Net is arguably the most successful segmentation architecture in t...

0 Fabian Isensee, et al. ∙

research

∙ 07/09/2019

Deep Probabilistic Modeling of Glioma Growth

Existing approaches to modeling the dynamics of brain tumor growth, spec...

1 Jens Petersen, et al. ∙

research

∙ 07/04/2019

Unsupervised Anomaly Localization using Variational Auto-Encoders

An assumption-free automatic check of medical images for potentially ove...

0 David Zimmerer, et al. ∙

research

∙ 04/17/2019

nnU-Net: Breaking the Spell on Successful Medical Image Segmentation

Fueled by the diversity of datasets, semantic segmentation is a popular ...

0 Fabian Isensee, et al. ∙

research

∙ 01/31/2019

Automated brain extraction of multi-sequence MRI using artificial neural networks

Brain extraction is a critical preprocessing step in the analysis of MRI...

0 Fabian Isensee, et al. ∙

research

∙ 12/14/2018

Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection

Unsupervised learning can leverage large-scale data sources without the ...

0 David Zimmerer, et al. ∙

research

∙ 11/21/2018

Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection

The task of localizing and categorizing objects in medical images often ...

38 Paul F. Jaeger, 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

∙ 09/27/2018

nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

The U-Net was presented in 2015. With its straight-forward and successfu...

0 Fabian Isensee, et al. ∙

research

∙ 09/27/2018

No New-Net

In this paper we demonstrate the effectiveness of a well trained U-Net i...

0 Fabian Isensee, et al. ∙

research

∙ 02/28/2018

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

Quantitative analysis of brain tumors is critical for clinical decision ...

2 Fabian Isensee, et al. ∙

research

∙ 11/27/2017

Exploiting the potential of unlabeled endoscopic video data with self-supervised learning

Purpose: Due to the breakthrough successes of deep learning-based soluti...

0 Tobias Ross, et al. ∙

research

∙ 07/03/2017

Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features

Cardiac magnetic resonance imaging improves on diagnosis of cardiovascul...

0 Fabian Isensee, et al. ∙

research

∙ 03/06/2017

Direct White Matter Bundle Segmentation using Stacked U-Nets

The state-of-the-art method for automatically segmenting white matter bu...

0 Jakob Wasserthal, et al. ∙

research

∙ 11/25/2016

Clickstream analysis for crowd-based object segmentation with confidence

With the rapidly increasing interest in machine learning based solutions...

0 Eric Heim, et al. ∙

Fabian Isensee

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