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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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