Klaus H. Maier-Hein

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

∙ 06/27/2023

Taming Detection Transformers for Medical Object Detection

The accurate detection of suspicious regions in medical images is an err...

0 Marc K. Ickler, et al. ∙

research

∙ 04/10/2023

SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model

Foundation models have taken over natural language processing and image ...

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

∙ 03/20/2023

Accurate Detection of Mediastinal Lesions with nnDetection

The accurate detection of mediastinal lesions is one of the rarely explo...

0 Michael Baumgartner, 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

∙ 11/04/2022

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most a...

6 M. Jorge Cardoso, et al. ∙

research

∙ 10/07/2022

Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding

In clinical radiology reports, doctors capture important information abo...

0 Constantin Seibold, 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

∙ 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

∙ 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

∙ 11/28/2019

A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients

Through training on unlabeled data, anomaly detection has the potential ...

0 David Zimmerer, 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

∙ 05/30/2019

A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities

Medical imaging only indirectly measures the molecular identity of the t...

5 Simon A. A. Kohl, 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

∙ 01/29/2019

Combined tract segmentation and orientation mapping for bundle-specific tractography

While the major white matter tracts are of great interest to numerous st...

0 Jakob Wasserthal, 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

∙ 07/17/2018

Domain Adaptation for Deviating Acquisition Protocols in CNN-based Lesion Classification on Diffusion-Weighted MR Images

End-to-end deep learning improves breast cancer classification on diffus...

0 Jennifer Kamphenkel, et al. ∙

research

∙ 06/14/2018

Tract orientation mapping for bundle-specific tractography

While the major white matter tracts are of great interest to numerous st...

0 Jakob Wasserthal, et al. ∙

research

∙ 06/13/2018

A Probabilistic U-Net for Segmentation of Ambiguous Images

Many real-world vision problems suffer from inherent ambiguities. In cli...

0 Simon A. A. Kohl, et al. ∙

research

∙ 05/18/2018

TractSeg - Fast and accurate white matter tract segmentation

The individual course of white matter fiber tracts is an important key f...

0 Jakob Wasserthal, et al. ∙

research

∙ 04/03/2018

Towards whole-body CT Bone Segmentation

Bone segmentation from CT images is a task that has been worked on for d...

0 André Klein, 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

∙ 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

∙ 02/27/2017

Revealing Hidden Potentials of the q-Space Signal in Breast Cancer

Mammography screening for early detection of breast lesions currently su...

0 Paul Jaeger, et al. ∙

Klaus H. Maier-Hein

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