Search Results for author: Jens Kleesiek

Found 73 papers, 34 papers with code

Fine-tuning BERT Models for Summarizing German Radiology Findings

no code implementations NAACL (ClinicalNLP) 2022 Siting Liang, Klaus Kades, Matthias Fink, Peter Full, Tim Weber, Jens Kleesiek, Michael Strube, Klaus Maier-Hein

Writing the conclusion section of radiology reports is essential for communicating the radiology findings and its assessment to physician in a condensed form.

Decoder

A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment

no code implementations15 May 2025 Jean-Philippe Corbeil, Amin Dada, Jean-Michel Attendu, Asma Ben Abacha, Alessandro Sordoni, Lucas Caccia, François Beaulieu, Thomas Lin, Jens Kleesiek, Paul Vozila

We introduce the MediPhi collection of 3. 8B-parameter SLMs developed with our novel framework: pre-instruction tuning of experts on relevant medical and clinical corpora (PMC, Medical Guideline, MedWiki, etc.

Domain Adaptation

PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation

1 code implementation10 Apr 2025 Moritz Rempe, Fabian Hörst, Helmut Becker, Marco Schlimbach, Lukas Rotkopf, Kevin Kröninger, Jens Kleesiek

In this work, we introduce $\textit{PhaseGen}$, a novel complex-valued diffusion model for generating synthetic MRI raw data conditioned on magnitude images, commonly used in clinical practice.

Diagnostic MRI Reconstruction +2

Towards Conditioning Clinical Text Generation for User Control

no code implementations24 Feb 2025 Osman Alperen Koraş, Rabi Bahnan, Jens Kleesiek, Amin Dada

Deploying natural language generation systems in clinical settings remains challenging despite advances in Large Language Models (LLMs), which continue to exhibit hallucinations and factual inconsistencies, necessitating human oversight.

Text Generation

Foreign object segmentation in chest x-rays through anatomy-guided shape insertion

no code implementations21 Jan 2025 Constantin Seibold, Hamza Kalisch, Lukas Heine, Simon Reiß, Jens Kleesiek

In this paper, we tackle the challenge of instance segmentation for foreign objects in chest radiographs, commonly seen in postoperative follow-ups with stents, pacemakers, or ingested objects in children.

Anatomy Diversity +2

Unlocking the Potential of Digital Pathology: Novel Baselines for Compression

no code implementations17 Dec 2024 Maximilian Fischer, Peter Neher, Peter Schüffler, Sebastian Ziegler, Shuhan Xiao, Robin Peretzke, David Clunie, Constantin Ulrich, Michael Baumgartner, Alexander Muckenhuber, Silvia Dias Almeida, Michael Götz, Jens Kleesiek, Marco Nolden, Rickmer Braren, Klaus Maier-Hein

While prior research addresses perceptual image quality and downstream performance independently of each other, we jointly evaluate compression schemes for perceptual and downstream task quality on four different datasets.

whole slide images

Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy

1 code implementation22 Nov 2024 Nikoo Moradi, André Ferreira, Behrus Puladi, Jens Kleesiek, Emad Fatemizadeh, Gijs Luijten, Victor Alves, Jan Egger

We utilized the HNTS-MRG2024 dataset, which consists of 150 MRI scans from patients diagnosed with HNC, including original and registered pre-RT and mid-RT T2-weighted images with corresponding segmentation masks for GTVp and GTVn.

Segmentation Task 2 +1

Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation

1 code implementation7 Nov 2024 André Ferreira, Tiago Jesus, Behrus Puladi, Jens Kleesiek, Victor Alves, Jan Egger

The use of automated tools in clinical practice has increased due to the development of more and more sophisticated and reliable algorithms.

Data Augmentation Synthetic Data Generation

Brain Tumour Removing and Missing Modality Generation using 3D WDM

1 code implementation7 Nov 2024 André Ferreira, Gijs Luijten, Behrus Puladi, Jens Kleesiek, Victor Alves, Jan Egger

The lack of information that is usually provided by some of the missing MRI modalities also reduces the reliability of the prediction models trained with all modalities.

Prediction SSIM

Every Component Counts: Rethinking the Measure of Success for Medical Semantic Segmentation in Multi-Instance Segmentation Tasks

no code implementations24 Oct 2024 Alexander Jaus, Constantin Seibold, Simon Reiß, Zdravko Marinov, Keyi Li, Zeling Ye, Stefan Krieg, Jens Kleesiek, Rainer Stiefelhagen

We present Connected-Component~(CC)-Metrics, a novel semantic segmentation evaluation protocol, targeted to align existing semantic segmentation metrics to a multi-instance detection scenario in which each connected component matters.

Instance Segmentation Segmentation +1

De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy

2 code implementations16 Oct 2024 Moritz Rempe, Lukas Heine, Constantin Seibold, Fabian Hörst, Jens Kleesiek

Medical data employed in research frequently comprises sensitive patient health information (PHI), which is subject to rigorous legal frameworks such as the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA).

De-identification whole slide images

Spacewalker: Traversing Representation Spaces for Fast Interactive Exploration and Annotation of Unstructured Data

1 code implementation25 Sep 2024 Lukas Heine, Fabian Hörst, Jana Fragemann, Gijs Luijten, Jan Egger, Fin Bahnsen, M. Saquib Sarfraz, Jens Kleesiek, Constantin Seibold

In industries such as healthcare, finance, and manufacturing, analysis of unstructured textual data presents significant challenges for analysis and decision making.

Decision Making

Towards Synthetic Data Generation for Improved Pain Recognition in Videos under Patient Constraints

1 code implementation24 Sep 2024 Jonas Nasimzada, Jens Kleesiek, Ken Herrmann, Alina Roitberg, Constantin Seibold

Utilizing advanced facial capture techniques, and leveraging public datasets like CelebV-HQ and FFHQ-UV for demographic diversity, our new synthetic dataset significantly enhances model training while ensuring privacy by anonymizing identities through facial replacements.

Dataset Generation Privacy Preserving +1

Autopet III challenge: Incorporating anatomical knowledge into nnUNet for lesion segmentation in PET/CT

1 code implementation18 Sep 2024 Hamza Kalisch, Fabian Hörst, Ken Herrmann, Jens Kleesiek, Constantin Seibold

The autoPET III Challenge focuses on advancing automated segmentation of tumor lesions in PET/CT images in a multitracer multicenter setting, addressing the clinical need for quantitative, robust, and generalizable solutions.

Diagnostic Lesion Segmentation +1

Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution

no code implementations18 Jun 2024 Maximilian Fischer, Peter Neher, Tassilo Wald, Silvia Dias Almeida, Shuhan Xiao, Peter Schüffler, Rickmer Braren, Michael Götz, Alexander Muckenhuber, Jens Kleesiek, Marco Nolden, Klaus Maier-Hein

In this paper, we show that the commonly used JPEG algorithm is not best suited for further compression and we propose Stain Quantized Latent Compression (SQLC ), a novel DL based histopathology data compression approach.

Data Compression Image Compression +3

Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain

no code implementations15 May 2024 Markus R. Bujotzek, Ünal Akünal, Stefan Denner, Peter Neher, Maximilian Zenk, Eric Frodl, Astha Jaiswal, Moon Kim, Nicolai R. Krekiehn, Manuel Nickel, Richard Ruppel, Marcus Both, Felix Döllinger, Marcel Opitz, Thorsten Persigehl, Jens Kleesiek, Tobias Penzkofer, Klaus Maier-Hein, Rickmer Braren, Andreas Bucher

Our results underscore the value of efforts needed to translate FL into real-world applications by demonstrating advantageous performance over alternatives, and emphasize the importance of strategic organization, robust management of distributed data and infrastructure in real-world settings.

Federated Learning

Deep Learning-based Point Cloud Registration for Augmented Reality-guided Surgery

no code implementations6 May 2024 Maximilian Weber, Daniel Wild, Jens Kleesiek, Jan Egger, Christina Gsaxner

We created a dataset of point clouds from medical imaging and corresponding point clouds captured with a popular AR device, the HoloLens 2.

Deep Learning Point Cloud Registration

Does Biomedical Training Lead to Better Medical Performance?

1 code implementation5 Apr 2024 Amin Dada, Marie Bauer, Amanda Butler Contreras, Osman Alperen Koraş, Constantin Marc Seibold, Kaleb E Smith, Jens Kleesiek

This study investigates the effect of biomedical training in the context of six practical medical tasks evaluating $25$ models.

Rethinking Annotator Simulation: Realistic Evaluation of Whole-Body PET Lesion Interactive Segmentation Methods

no code implementations2 Apr 2024 Zdravko Marinov, Moon Kim, Jens Kleesiek, Rainer Stiefelhagen

In an initial user study involving four annotators, we assess existing robot users using our proposed metrics and find that robot users significantly deviate in performance and annotation behavior compared to real annotators.

Interactive Segmentation Segmentation

DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images

no code implementations12 Mar 2024 Michael Götz, Christian Weber, Franciszek Binczyk, Joanna Polanska, Rafal Tarnawski, Barbara Bobek-Billewicz, Ullrich Köthe, Jens Kleesiek, Bram Stieltjes, Klaus H. Maier-Hein

We propose a new method that employs transfer learning techniques to effectively correct sampling selection errors introduced by sparse annotations during supervised learning for automated tumor segmentation.

Domain Adaptation Transfer Learning +1

Deep PCCT: Photon Counting Computed Tomography Deep Learning Applications Review

no code implementations6 Feb 2024 Ana Carolina Alves, André Ferreira, Gijs Luijten, Jens Kleesiek, Behrus Puladi, Jan Egger, Victor Alves

This review delves into the recent developments and applications of PCCT in pre-clinical research, emphasizing its potential to overcome traditional imaging limitations.

Articles Deep Learning +1

Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy

no code implementations23 Nov 2023 Zdravko Marinov, Paul F. Jäger, Jan Egger, Jens Kleesiek, Rainer Stiefelhagen

Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback.

Interactive Segmentation Medical Image Analysis

Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning

no code implementations9 Oct 2023 Amr Abourayya, Jens Kleesiek, Kanishka Rao, Erman Ayday, Bharat Rao, Geoff Webb, Michael Kamp

We propose a federated co-training (FedCT) approach that improves privacy by sharing only definitive (hard) labels on a public unlabeled dataset.

Federated Learning

Multilingual Natural Language Processing Model for Radiology Reports -- The Summary is all you need!

no code implementations29 Sep 2023 Mariana Lindo, Ana Sofia Santos, André Ferreira, Jianning Li, Gijs Luijten, Gustavo Correia, Moon Kim, Benedikt Michael Schaarschmidt, Cornelius Deuschl, Johannes Haubold, Jens Kleesiek, Jan Egger, Victor Alves

In this study, the generation of radiology impressions in different languages was automated by fine-tuning a model, publicly available, based on a multilingual text-to-text Transformer to summarize findings available in English, Portuguese, and German radiology reports.

All

Anatomy Completor: A Multi-class Completion Framework for 3D Anatomy Reconstruction

1 code implementation10 Sep 2023 Jianning Li, Antonio Pepe, Gijs Luijten, Christina Schwarz-Gsaxner, Jens Kleesiek, Jan Egger

We propose two paradigms based on a 3D denoising auto-encoder (DAE) to solve the anatomy reconstruction problem: (i) the DAE learns a many-to-one mapping between incomplete and complete instances; (ii) the DAE learns directly a one-to-one residual mapping between the incomplete instances and the target anatomies.

Anatomy Denoising

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

1 code implementation30 Aug 2023 Jianning Li, Zongwei Zhou, Jiancheng Yang, Antonio Pepe, Christina Gsaxner, Gijs Luijten, Chongyu Qu, Tiezheng Zhang, Xiaoxi Chen, Wenxuan Li, Marek Wodzinski, Paul Friedrich, Kangxian Xie, Yuan Jin, Narmada Ambigapathy, Enrico Nasca, Naida Solak, Gian Marco Melito, Viet Duc Vu, Afaque R. Memon, Christopher Schlachta, Sandrine de Ribaupierre, Rajnikant Patel, Roy Eagleson, Xiaojun Chen, Heinrich Mächler, Jan Stefan Kirschke, Ezequiel de la Rosa, Patrick Ferdinand Christ, Hongwei Bran Li, David G. Ellis, Michele R. Aizenberg, Sergios Gatidis, Thomas Küstner, Nadya Shusharina, Nicholas Heller, Vincent Andrearczyk, Adrien Depeursinge, Mathieu Hatt, Anjany Sekuboyina, Maximilian Löffler, Hans Liebl, Reuben Dorent, Tom Vercauteren, Jonathan Shapey, Aaron Kujawa, Stefan Cornelissen, Patrick Langenhuizen, Achraf Ben-Hamadou, Ahmed Rekik, Sergi Pujades, Edmond Boyer, Federico Bolelli, Costantino Grana, Luca Lumetti, Hamidreza Salehi, Jun Ma, Yao Zhang, Ramtin Gharleghi, Susann Beier, Arcot Sowmya, Eduardo A. Garza-Villarreal, Thania Balducci, Diego Angeles-Valdez, Roberto Souza, Leticia Rittner, Richard Frayne, Yuanfeng Ji, Vincenzo Ferrari, Soumick Chatterjee, Florian Dubost, Stefanie Schreiber, Hendrik Mattern, Oliver Speck, Daniel Haehn, Christoph John, Andreas Nürnberger, João Pedrosa, Carlos Ferreira, Guilherme Aresta, António Cunha, Aurélio Campilho, Yannick Suter, Jose Garcia, Alain Lalande, Vicky Vandenbossche, Aline Van Oevelen, Kate Duquesne, Hamza Mekhzoum, Jef Vandemeulebroucke, Emmanuel Audenaert, Claudia Krebs, Timo Van Leeuwen, Evie Vereecke, Hauke Heidemeyer, Rainer Röhrig, Frank Hölzle, Vahid Badeli, Kathrin Krieger, Matthias Gunzer, Jianxu Chen, Timo van Meegdenburg, Amin Dada, Miriam Balzer, Jana Fragemann, Frederic Jonske, Moritz Rempe, Stanislav Malorodov, Fin H. Bahnsen, Constantin Seibold, Alexander Jaus, Zdravko Marinov, Paul F. Jaeger, Rainer Stiefelhagen, Ana Sofia Santos, Mariana Lindo, André Ferreira, Victor Alves, Michael Kamp, Amr Abourayya, Felix Nensa, Fabian Hörst, Alexander Brehmer, Lukas Heine, Yannik Hanusrichter, Martin Weßling, Marcel Dudda, Lars E. Podleska, Matthias A. Fink, Julius Keyl, Konstantinos Tserpes, Moon-Sung Kim, Shireen Elhabian, Hans Lamecker, Dženan Zukić, Beatriz Paniagua, Christian Wachinger, Martin Urschler, Luc Duong, Jakob Wasserthal, Peter F. Hoyer, Oliver Basu, Thomas Maal, Max J. H. Witjes, Gregor Schiele, Ti-chiun Chang, Seyed-Ahmad Ahmadi, Ping Luo, Bjoern Menze, Mauricio Reyes, Thomas M. Deserno, Christos Davatzikos, Behrus Puladi, Pascal Fua, Alan L. Yuille, Jens Kleesiek, Jan Egger

For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.

Anatomy Mixed Reality

Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines

1 code implementation25 Jul 2023 Alexander Jaus, Constantin Seibold, Kelsey Hermann, Alexandra Walter, Kristina Giske, Johannes Haubold, Jens Kleesiek, Rainer Stiefelhagen

We examine its plausibility and usefulness using three complementary checks: Human expert evaluation which approved the dataset, a Deep Learning usefulness benchmark on the BTCV dataset in which we achieve 85% dice score without using its training dataset, and medical validity checks.

Anatomy Pseudo Label +1

FAM: Relative Flatness Aware Minimization

1 code implementation5 Jul 2023 Linara Adilova, Amr Abourayya, Jianning Li, Amin Dada, Henning Petzka, Jan Egger, Jens Kleesiek, Michael Kamp

Their widespread adoption in practice, though, is dubious because of the lack of theoretically grounded connection between flatness and generalization, in particular in light of the reparameterization curse - certain reparameterizations of a neural network change most flatness measures but do not change generalization.

CellViT: Vision Transformers for Precise Cell Segmentation and Classification

3 code implementations27 Jun 2023 Fabian Hörst, Moritz Rempe, Lukas Heine, Constantin Seibold, Julius Keyl, Giulia Baldini, Selma Ugurel, Jens Siveke, Barbara Grünwald, Jan Egger, Jens Kleesiek

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications.

Cell Detection Cell Segmentation +4

Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling

1 code implementation6 Jun 2023 Constantin Seibold, Alexander Jaus, Matthias A. Fink, Moon Kim, Simon Reiß, Ken Herrmann, Jens Kleesiek, Rainer Stiefelhagen

Results: Our resulting segmentation models demonstrated remarkable performance on CXR, with a high average model-annotator agreement between two radiologists with mIoU scores of 0. 93 and 0. 85 for frontal and lateral anatomy, while inter-annotator agreement remained at 0. 95 and 0. 83 mIoU.

Anatomy Computed Tomography (CT) +2

Guiding the Guidance: A Comparative Analysis of User Guidance Signals for Interactive Segmentation of Volumetric Images

no code implementations13 Mar 2023 Zdravko Marinov, Rainer Stiefelhagen, Jens Kleesiek

To address this, we conduct a comparative study of existing guidance signals by training interactive models with different signals and parameter settings to identify crucial parameters for the model's design.

Anatomy Interactive Segmentation +1

Understanding metric-related pitfalls in image analysis validation

no code implementations3 Feb 2023 Annika Reinke, Minu D. Tizabi, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, A. Emre Kavur, Tim Rädsch, Carole H. Sudre, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, Matthew Blaschko, Florian Buettner, M. Jorge Cardoso, Veronika Cheplygina, Jianxu Chen, Evangelia Christodoulou, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Jens Kleesiek, Florian Kofler, Thijs Kooi, Annette Kopp-Schneider, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Susanne M. Rafelski, Nasir Rajpoot, Mauricio Reyes, Michael A. Riegler, Nicola Rieke, Julio Saez-Rodriguez, Clara I. Sánchez, Shravya Shetty, Maarten van Smeden, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul F. Jäger, Lena Maier-Hein

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.

Open-Source Skull Reconstruction with MONAI

1 code implementation25 Nov 2022 Jianning Li, André Ferreira, Behrus Puladi, Victor Alves, Michael Kamp, Moon-Sung Kim, Felix Nensa, Jens Kleesiek, Seyed-Ahmad Ahmadi, Jan Egger

The primary goal of this paper lies in the investigation of open-sourcing codes and pre-trained deep learning models under the MONAI framework.

C++ code Deep Learning +1

Valuing Vicinity: Memory attention framework for context-based semantic segmentation in histopathology

1 code implementation21 Oct 2022 Oliver Ester, Fabian Hörst, Constantin Seibold, Julius Keyl, Saskia Ting, Nikolaos Vasileiadis, Jessica Schmitz, Philipp Ivanyi, Viktor Grünwald, Jan Hinrich Bräsen, Jan Egger, Jens Kleesiek

The segmentation of histopathological whole slide images into tumourous and non-tumourous types of tissue is a challenging task that requires the consideration of both local and global spatial contexts to classify tumourous regions precisely.

Decoder Segmentation +2

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

no code implementations7 Oct 2022 Constantin Seibold, Simon Reiß, Saquib Sarfraz, Matthias A. Fink, Victoria Mayer, Jan Sellner, Moon Sung Kim, Klaus H. Maier-Hein, Jens Kleesiek, Rainer Stiefelhagen

To exploit anatomical structures in this scenario, we present a sophisticated automatic pipeline to gather and integrate human bodily structures from computed tomography datasets, which we incorporate in our PAXRay: A Projected dataset for the segmentation of Anatomical structures in X-Ray data.

Anatomy Phrase Grounding

Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder

1 code implementation29 Sep 2022 Jianning Li, Jana Fragemann, Seyed-Ahmad Ahmadi, Jens Kleesiek, Jan Egger

The reconstruction loss and the Kullback-Leibler divergence (KLD) loss in a variational autoencoder (VAE) often play antagonistic roles, and tuning the weight of the KLD loss in $\beta$-VAE to achieve a balance between the two losses is a tricky and dataset-specific task.

Decoder Disentanglement

The HoloLens in Medicine: A systematic Review and Taxonomy

no code implementations6 Sep 2022 Christina Gsaxner, Jianning Li, Antonio Pepe, Yuan Jin, Jens Kleesiek, Dieter Schmalstieg, Jan Egger

The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality display, is the main player in the recent boost in medical augmented reality research.

Metrics reloaded: Recommendations for image analysis validation

1 code implementation3 Jun 2022 Lena Maier-Hein, Annika Reinke, Patrick Godau, Minu D. Tizabi, Florian Buettner, Evangelia Christodoulou, Ben Glocker, Fabian Isensee, Jens Kleesiek, Michal Kozubek, Mauricio Reyes, Michael A. Riegler, Manuel Wiesenfarth, A. Emre Kavur, Carole H. Sudre, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, Tim Rädsch, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, Matthew Blaschko, M. Jorge Cardoso, Veronika Cheplygina, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Robert Haase, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Florian Kofler, Annette Kopp-Schneider, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Nasir Rajpoot, Nicola Rieke, Julio Saez-Rodriguez, Clara I. Sánchez, Shravya Shetty, Maarten van Smeden, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Paul F. Jäger

The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.

Instance Segmentation Medical Image Analysis +3

Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model

1 code implementation12 Apr 2022 Jianning Li, David G. Ellis, Antonio Pepe, Christina Gsaxner, Michele R. Aizenberg, Jens Kleesiek, Jan Egger

We evaluate the SSM on several cranial implant design tasks, and the results show that, while the SSM performs suboptimally on synthetic defects compared to CNN-based approaches, it is capable of reconstructing large and complex defects with only minor manual corrections.

Data Augmentation

Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation

no code implementations1 Dec 2021 Constantin Seibold, Simon Reiß, Jens Kleesiek, Rainer Stiefelhagen

Following this thought, we use a small number of labeled images as reference material and match pixels in an unlabeled image to the semantics of the best fitting pixel in a reference set.

Anatomy Pseudo Label +2

MOMO -- Deep Learning-driven classification of external DICOM studies for PACS archivation

no code implementations1 Dec 2021 Frederic Jonske, Maximilian Dederichs, Moon-Sung Kim, Jan Egger, Lale Umutlu, Michael Forsting, Felix Nensa, Jens Kleesiek

Additionally, an ablation study was performed to measure the performance impact of the network ensemble in the algorithm, and a comparative performance test with a commercial product was conducted.

AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo

no code implementations6 Aug 2021 Yuan Jin, Antonio Pepe, Jianning Li, Christina Gsaxner, Fen-hua Zhao, Kelsey L. Pomykala, Jens Kleesiek, Alejandro F. Frangi, Jan Egger

The standard imaging modality for diagnosis and monitoring is computed tomography (CT), which can provide a detailed picture of the aorta and its branching vessels if completed with a contrast agent, called CT angiography (CTA).

Computed Tomography (CT)

The Federated Tumor Segmentation (FeTS) Challenge

2 code implementations12 May 2021 Sarthak Pati, Ujjwal Baid, Maximilian Zenk, Brandon Edwards, Micah Sheller, G. Anthony Reina, Patrick Foley, Alexey Gruzdev, Jason Martin, Shadi Albarqouni, Yong Chen, Russell Taki Shinohara, Annika Reinke, David Zimmerer, John B. Freymann, Justin S. Kirby, Christos Davatzikos, Rivka R. Colen, Aikaterini Kotrotsou, Daniel Marcus, Mikhail Milchenko, Arash Nazer, Hassan Fathallah-Shaykh, Roland Wiest, Andras Jakab, Marc-Andre Weber, Abhishek Mahajan, Lena Maier-Hein, Jens Kleesiek, Bjoern Menze, Klaus Maier-Hein, Spyridon Bakas

The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i. e. on data from institutional distributions that were not part of the training datasets.

Brain Tumor Segmentation Federated Learning +2

Common Limitations of Image Processing Metrics: A Picture Story

1 code implementation12 Apr 2021 Annika Reinke, Minu D. Tizabi, Carole H. Sudre, Matthias Eisenmann, Tim Rädsch, Michael Baumgartner, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, Matthew Blaschko, Florian Buettner, M. Jorge Cardoso, Jianxu Chen, Veronika Cheplygina, Evangelia Christodoulou, Beth Cimini, Gary S. Collins, Sandy Engelhardt, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Fred Hamprecht, Daniel A. Hashimoto, Doreen Heckmann-Nötzel, Peter Hirsch, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, A. Emre Kavur, Hannes Kenngott, Jens Kleesiek, Andreas Kleppe, Sven Kohler, Florian Kofler, Annette Kopp-Schneider, Thijs Kooi, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, David Moher, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, M. Alican Noyan, Jens Petersen, Gorkem Polat, Susanne M. Rafelski, Nasir Rajpoot, Mauricio Reyes, Nicola Rieke, Michael Riegler, Hassan Rivaz, Julio Saez-Rodriguez, Clara I. Sánchez, Julien Schroeter, Anindo Saha, M. Alper Selver, Lalith Sharan, Shravya Shetty, Maarten van Smeden, Bram Stieltjes, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul Jäger, Lena Maier-Hein

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.

Instance Segmentation object-detection +2

A Relational-learning Perspective to Multi-label Chest X-ray Classification

no code implementations10 Mar 2021 Anjany Sekuboyina, Daniel Oñoro-Rubio, Jens Kleesiek, Brandon Malone

Multi-label classification of chest X-ray images is frequently performed using discriminative approaches, i. e. learning to map an image directly to its binary labels.

Classification General Classification +6

Medical Deep Learning -- A systematic Meta-Review

no code implementations28 Oct 2020 Jan Egger, Christina Gsaxner, Antonio Pepe, Kelsey L. Pomykala, Frederic Jonske, Manuel Kurz, Jianning Li, Jens Kleesiek

With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for automated and reliable processing and analysis of health information.

Articles Autonomous Driving +2

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