Fabian Isensee

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Ph.D. Student at German Cancer Research Center

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...
04/27/2020 ∙ by Fabian Isensee, et al. ∙ 0 ∙ share

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Robust Medical Instrument Segmentation Challenge 2019

Intraoperative tracking of laparoscopic instruments is often a prerequis...
03/23/2020 ∙ by Tobias Ross, et al. ∙ 13 ∙ share

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

Segmentation of abdominal organs has been a comprehensive, yet unresolve...
01/17/2020 ∙ by A. Emre Kavur, et al. ∙ 0 ∙ share

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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...
12/02/2019 ∙ by Nicholas Heller, et al. ∙ 0 ∙ share

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ModelHub.AI: Dissemination Platform for Deep Learning Models

Recent advances in artificial intelligence research have led to a profus...
11/26/2019 ∙ by Ahmed Hosny, et al. ∙ 0 ∙ share

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An attempt at beating the 3D U-Net

The U-Net is arguably the most successful segmentation architecture in t...
08/06/2019 ∙ by Fabian Isensee, et al. ∙ 0 ∙ share

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Deep Probabilistic Modeling of Glioma Growth

Existing approaches to modeling the dynamics of brain tumor growth, spec...
07/09/2019 ∙ by Jens Petersen, et al. ∙ 1 ∙ share

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Unsupervised Anomaly Localization using Variational Auto-Encoders

An assumption-free automatic check of medical images for potentially ove...
07/04/2019 ∙ by David Zimmerer, et al. ∙ 0 ∙ share

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

Fueled by the diversity of datasets, semantic segmentation is a popular ...
04/17/2019 ∙ by Fabian Isensee, et al. ∙ 0 ∙ share

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

Brain extraction is a critical preprocessing step in the analysis of MRI...
01/31/2019 ∙ by Fabian Isensee, et al. ∙ 0 ∙ share

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Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection

Unsupervised learning can leverage large-scale data sources without the ...
12/14/2018 ∙ by David Zimmerer, et al. ∙ 0 ∙ share

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

The task of localizing and categorizing objects in medical images often ...
11/21/2018 ∙ by Paul F. Jaeger, et al. ∙ 38 ∙ share

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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...
11/05/2018 ∙ by Spyridon Bakas, et al. ∙ 0 ∙ share

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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...
09/27/2018 ∙ by Fabian Isensee, et al. ∙ 0 ∙ share

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No New-Net

In this paper we demonstrate the effectiveness of a well trained U-Net i...
09/27/2018 ∙ by Fabian Isensee, et al. ∙ 0 ∙ share

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

Quantitative analysis of brain tumors is critical for clinical decision ...
02/28/2018 ∙ by Fabian Isensee, et al. ∙ 2 ∙ share

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

Purpose: Due to the breakthrough successes of deep learning-based soluti...
11/27/2017 ∙ by Tobias Ross, et al. ∙ 0 ∙ share

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

Cardiac magnetic resonance imaging improves on diagnosis of cardiovascul...
07/03/2017 ∙ by Fabian Isensee, et al. ∙ 0 ∙ share

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Direct White Matter Bundle Segmentation using Stacked U-Nets

The state-of-the-art method for automatically segmenting white matter bu...
03/06/2017 ∙ by Jakob Wasserthal, et al. ∙ 0 ∙ share

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Clickstream analysis for crowd-based object segmentation with confidence

With the rapidly increasing interest in machine learning based solutions...
11/25/2016 ∙ by Eric Heim, et al. ∙ 0 ∙ share

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Fabian Isensee

Featured Co-authors

Dacheng Tao

Wei Chen

Chang Liu

Pheng-Ann Heng

Qian Wang

Yu Sun

Kurt Keutzer

Kun Zhang

Sébastien Ourselin

Lu Wang

Lei Zhu

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

Robust Medical Instrument Segmentation Challenge 2019

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

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

ModelHub.AI: Dissemination Platform for Deep Learning Models

An attempt at beating the 3D U-Net

Deep Probabilistic Modeling of Glioma Growth

Unsupervised Anomaly Localization using Variational Auto-Encoders

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

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

Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection

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

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

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

No New-Net

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

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

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

Direct White Matter Bundle Segmentation using Stacked U-Nets

Clickstream analysis for crowd-based object segmentation with confidence