Analysis of Mitral Valve Motion in 4D Transesophageal Echocardiography for Transcatheter Aortic Valve Implantation. - Frank M. Weber; Thomas Stehle; Irina Wächter-Stehle; Michael Götz; Jochen Peters; Sabine Mollus; Jan Balzer; Malte Kelm; Jürgen Weese (STACOM, 2014)

Using Linked Data and Web APIs for Automating the Pre-processing of Medical Images. - Philipp Gemmeke; Maria Maleshkova; Patrick Philipp 002; Michael Götz; Christian Weber 001; Benedikt Kämpgen; Marco Nolden; Klaus H. Maier-Hein; Achim Rettinger (COLD, 2014)

Automatische Tumorsegmentierung mit spärlich annotierter Lernbasis. - Michael Götz; Christian Weber 001; Franciszek Binczyk; Joanna Polanska; Rafal Tarnawski; Barbara Bobek-Billewicz; Hans-Peter Meinzer; Bram Stieltjes; Klaus H. Maier-Hein (Bildverarbeitung für die Medizin, 2015)

Automatisierung von Vorverarbeitungsschritten für medizinische Bilddaten mit semantischen Technologien. - Patrick Philipp 002; Maria Maleshkova; Michael Götz; Christian Weber 001; Benedikt Kämpgen; Sascha Zelzer; Klaus H. Maier-Hein; Miriam Klauß; Achim Rettinger (Bildverarbeitung für die Medizin, 2015)

Überwachtes Lernen zur Prädiktion von Tumorwachstum. - Christian Weber 001; Michael Götz; Franciszek Binczyk; Joanna Polanska; Rafal Tarnawski; Barbara Bobek-Billewicz; Hans-Peter Meinzer; Bram Stieltjes; Klaus H. Maier-Hein (Bildverarbeitung für die Medizin, 2015)

Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion Segmentation. - Michael Götz; Christian Weber 001; Christoph Kolb; Klaus H. Maier-Hein (Brainles@MICCAI, 2015)

A Machine Learning Based Approach to Fiber Tractography Using Classifier Voting. - Peter F. Neher; Michael Götz; Tobias Norajitra; Christian Weber 001; Klaus H. Maier-Hein (MICCAI (1), 2015)

Fallspezifisches Lernen zur automatischen Läsionssegmentierung in multimodalen MR-Bildern. - Michael Götz; Christoph Kolb; Christian Weber 001; Sebastian Regnery; Klaus H. Maier-Hein (Bildverarbeitung für die Medizin, 2016)

Machine-learning based comparison of CT-perfusion maps and dual energy CT for pancreatic tumor detection. - Michael Götz; Stephan Skornitzke; Christian Weber 001; Franziska Fritz; Philipp Mayer; Marco Koell; Wolfram Stiller; Klaus H. Maier-Hein (Computer-Aided Diagnosis, 2016)

Crowd-Algorithm Collaboration for Large-Scale Endoscopic Image Annotation with Confidence. - Lena Maier-Hein; Tobias Roß; Janek Gröhl; Ben Glocker; Sebastian Bodenstedt; Christian Stock; Eric Heim; Michael Götz; Sebastian J. Wirkert; Hannes Kenngott; Stefanie Speidel; Klaus H. Maier-Hein (MICCAI (2), 2016)

A learning-based, fully automatic liver tumor segmentation pipeline based on sparsely annotated training data. - Michael Götz; Eric Heim; Keno März; Tobias Norajitra; Mohammadreza Hafezi; Nassim Fard; Arianeb Mehrabi; Max Knoll; Christian Weber 001; Lena Maier-Hein; Klaus H. Maier-Hein (Image Processing, 2016)

Toward cognitive pipelines of medical assistance algorithms. - Patrick Philipp 002; Maria Maleshkova; Darko Katic; Christian Weber 001; Michael Götz; Achim Rettinger; Stefanie Speidel; Benedikt Kämpgen; Marco Nolden; Anna-Laura Wekerle; Rüdiger Dillmann; Hannes Kenngott; et al. (Int. J. Comput. Assist. Radiol. Surg., 2016)

DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images. - Michael Götz; Christian Weber 001; Franciszek Binczyk; Joanna Polanska; Rafal Tarnawski; Barbara Bobek-Billewicz; Ullrich Köthe; Jens Kleesiek; Bram Stieltjes; Klaus H. Maier-Hein (IEEE Trans. Medical Imaging, 2016)

Training mit positiven und unannotierten Daten für automatische Voxelklassifikation. - Michael Götz; Klaus H. Maier-Hein (Bildverarbeitung für die Medizin, 2017)

Physiological Parameter Estimation from Multispectral Images Unleashed. - Sebastian J. Wirkert; Anant Suraj Vemuri; Hannes Götz Kenngott; Sara Moccia; Michael Götz; Benjamin F. B. Mayer; Klaus H. Maier-Hein; Daniel S. Elson; Lena Maier-Hein (MICCAI (3), 2017)

MiMSeg - an algorithm for automated detection of tumor tissue on NMR apparent diffusion coefficient maps. - Franciszek Binczyk; Bram Stjelties; Christian Weber 001; Michael Götz; Klaus H. Maier-Hein; Hans-Peter Meinzer; Barbara Bobek-Billewicz; Rafal Tarnawski; Joanna Polanska (Inf. Sci., 2017)

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. - Oskar Maier; Bjoern H. Menze; Janina von der Gablentz; Levin Häni; Mattias P. Heinrich; Matthias Liebrand; Stefan Winzeck; Abdul Basit 007; Paul Bentley; Liang Chen 018; Daan Christiaens; Francis Dutil; et al. (Medical Image Anal., 2017)

Variability-Aware and Weakly Supervised Learning for Semantic Tissue Segmentation. - Michael Götz (2017)

Automated Containerized Medical Image Processing Based on MITK and Python - A Modular System to Implement Medical Image Processing Pipelines and Visualize Meta Data. - Caspar J. Goch; Jasmin Metzger; Martin Hettich; André Klein; Tobias Norajitra; Michael Götz; Jens Petersen; Klaus H. Maier-Hein; Marco Nolden (Bildverarbeitung für die Medizin, 2018)

Abstract: Physiological Parameter Estimation from Multispectral Images Unleashed. - Sebastian J. Wirkert; Anant Suraj Vemuri; Hannes Götz Kenngott; Sara Moccia; Michael Götz; Benjamin F. B. Mayer; Klaus Hermann Maier-Hein; Daniel S. Elson; Lena Maier-Hein (Bildverarbeitung für die Medizin, 2018)

Abstract: Data Augmentation for Information Transfer - Why Controlling for Confounding Effects in Radiomic Studies is Important and How to do it. - Michael Götz; Klaus H. Maier-Hein (Bildverarbeitung für die Medizin, 2021)

Communities of Practice for Functional Learning in Agile Contexts: Definition Approach and Call for Research. - Atilla Wohllebe; Michael Götz (Int. J. Adv. Corp. Learn., 2021)

DICOM Whole Slide Imaging for Computational Pathology Research in Kaapana and the Joint Imaging Platform. - Maximilian Fischer; Philipp Schader; Rickmer Braren; Michael Götz; Alexander Muckenhuber; Wilko Weichert; Peter J. Schüffler; Jens Kleesiek; Jonas Scherer; Klaus Kades; Klaus H. Maier-Hein; Marco Nolden (Bildverarbeitung für die Medizin, 2022)

Deep Learning on Lossily Compressed Pathology Images: Adverse Effects for ImageNet Pre-trained Models. - Maximilian Fischer; Peter Neher; Michael Götz; Shuhan Xiao; Silvia Dias Almeida; Peter J. Schüffler; Alexander Muckenhuber; Rickmer Braren; Jens Kleesiek; Marco Nolden; Klaus H. Maier-Hein (MOVI@MICCAI, 2022)

Abstract: Deep-learning on Lossily Compressed Pathology Images - Adverse Effects for ImageNet Pre-trained Models. - Maximilian Fischer; Peter Neher; Michael Götz; Shuhan Xiao; Silvia Dias Almeida; Peter J. Schüffler; Alexander Muckenhuber; Rickmer Braren; Jens Kleesiek; Marco Nolden; Klaus H. Maier-Hein (Bildverarbeitung für die Medizin, 2023)

RPTK: The Role of Feature Computation on Prediction Performance. - Jonas R. Bohn; Christian M. Heidt; Silvia Dias Almeida; Lisa Kausch; Michael Götz; Marco Nolden; Petros Christopoulos; Stephan Rheinheimer; Alan A. Peters; Oyunbileg von Stackelberg; Hans-Ulrich Kauczor; Klaus H. Maier-Hein; et al. (MTSAIL/LEAF/AI4Treat/MMMI/REMIA@MICCAI, 2023)

Interpretable Medical Image Classification Using Prototype Learning and Privileged Information. - Luisa Gallée; Meinrad Beer; Michael Götz (MICCAI (2), 2023)

Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning. - Maximilian Fischer; Peter Neher; Peter J. Schüffler; Shuhan Xiao; Silvia Dias Almeida; Constantin Ulrich; Alexander Muckenhuber; Rickmer Braren; Michael Götz; Jens Kleesiek; Marco Nolden; Klaus H. Maier-Hein (MLMI@MICCAI (2), 2023)

Dealing with Small Datasets for Deep Learning in Medical Imaging: An Evaluation of Self-Supervised Pre-Training on CT Scans Comparing Contrastive and Masked Autoencoder Methods for Convolutional Models. - Daniel Wolf; Tristan Payer; Catharina Silvia Lisson; Christoph Gerhard Lisson; Meinrad Beer; Timo Ropinski; Michael Götz (CoRR, 2023)

On Leakage in Machine Learning Pipelines. - Leonard Sasse; Eliana Nicolaisen-Sobesky; Juergen Dukart; Simon B. Eickhoff; Michael Götz; Sami Hamdan; Vera Komeyer; Abhijit Kulkarni; Juha Lahnakoski; Bradley C. Love; Federico Raimondo; Kaustubh R. Patil (CoRR, 2023)

Abstract: Radiomics Processing Toolkit - Role of Feature Computation on Prediction Performance. - Jonas R. Bohn; Christian M. Heidt; Silvia Dias Almeida; Lisa Kausch; Michael Götz; Marco Nolden; Petros Christopoulos; Stephan Rheinheimer; Alan A. Peters; Oyunbileg von Stackelberg; Hans-Ulrich Kauczor; Klaus H. Maier-Hein; et al. (Bildverarbeitung für die Medizin, 2024)

Abstract: Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning. - Maximilian Fischer; Peter Neher; Peter J. Schüffler; Shuhan Xiao; Silvia Dias Almeida; Constantin Ulrich; Alexander Muckenhuber; Rickmer Braren; Michael Götz; Jens Kleesiek; Marco Nolden; Klaus H. Maier-Hein (Bildverarbeitung für die Medizin, 2024)

Abstract: Interpretable Medical Image Classification Using Prototype Learning and Privileged Information. - Luisa Gallée; Meinrad Beer; Michael Götz (Bildverarbeitung für die Medizin, 2024)

Abstract: Self-supervised Pre-training for Dealing with Small Datasets in Deep Learning for Medical Imaging - Evaluation of Contrastive and Masked Autoencoder Methods. - Daniel Wolf; Tristan Payer; Catharina Silvia Lisson; Christoph Gerhard Lisson; Meinrad Beer; Michael Götz; Timo Ropinski (Bildverarbeitung für die Medizin, 2024)

Learned Image Compression for HE-Stained Histopathological Images via Stain Deconvolution. - Maximilian Fischer; Peter Neher; Tassilo Wald; Silvia Dias Almeida; Shuhan Xiao; Peter J. Schüffler; Rickmer Braren; Michael Götz; Alexander Muckenhuber; Jens Kleesiek; Marco Nolden; Klaus H. Maier-Hein (MOVI@MICCAI, 2024)

Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model. - Luisa Gallée; Catharina Silvia Lisson; Christoph Gerhard Lisson; Daniela Drees; Felix Weig; Daniel Vogele; Meinrad Beer; Michael Götz (xAI (1), 2024)

Less is More: Selective reduction of CT data for self-supervised pre-training of deep learning models with contrastive learning improves downstream classification performance. - Daniel Wolf; Tristan Payer; Catharina Silvia Lisson; Christoph Gerhard Lisson; Meinrad Beer; Michael Götz; Timo Ropinski (Comput. Biol. Medicine, 2024)

Pre-examinations Improve Automated Metastases Detection on Cranial MRI. - Katerina Deike-Hofmann; Dorottya Dancs; Daniel Paech; Heinz-Peter Schlemmer; Klaus H. Maier-Hein; Philipp Bäumer; Alexander Radbruch; Michael Götz (CoRR, 2024)

Unlocking the Potential of Digital Pathology: Novel Baselines for Compression. - Maximilian Fischer; Peter Neher; Peter J. Schüffler; Sebastian Ziegler; Shuhan Xiao; Robin Peretzke; David A. Clunie; Constantin Ulrich; Michael Baumgartner 001; Alexander Muckenhuber; Silvia Dias Almeida; Michael Götz; et al. (CoRR, 2024)

Abstract: Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution. - Maximilian Fischer; Peter Neher; Tassilo Wald; Constantin Ulrich; Peter J. Schüffler; Shuhan Xiao; Silvia Dias Almeida; Alexander Muckenhuber; Rickmer Braren; Michael Götz; Jens Kleesiek; Marco Nolden; et al. (Bildverarbeitung für die Medizin, 2025)

Abstract: Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model. - Luisa Gallée; Catharina Silvia Lisson; Christoph Gerhard Lisson; Daniela Drees; Felix Weig; Daniel Vogele; Meinrad Beer; Michael Götz (Bildverarbeitung für die Medizin, 2025)

Abstract: Selective Reduction of CT Data for Self-supervised Pre-training Improves Downstream Classification Performance. - Daniel Wolf; Tristan Payer; Catharina Silvia Lisson; Christoph Gerhard Lisson; Meinrad Beer; Michael Götz; Timo Ropinski (Bildverarbeitung für die Medizin, 2025)

Proto-Caps: interpretable medical image classification using prototype learning and privileged information. - Luisa Gallée; Catharina Silvia Lisson; Timo Ropinski; Meinrad Beer; Michael Götz (PeerJ Comput. Sci., 2025)

Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification. - Luisa Gallée; Catharina Silvia Lisson; Meinrad Beer; Michael Götz (CoRR, 2025)

Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images. - Daniel Wolf; Heiko Hillenhagen; Billurvan Taskin; Alex Bäuerle; Meinrad Beer; Michael Götz; Timo Ropinski (CoRR, 2025)

Minimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification. - Luisa Gallée; Catharina Silvia Lisson; Christoph Gerhard Lisson; Daniela Drees; Felix Weig; Daniel Vogele; Meinrad Beer; Michael Götz (CoRR, 2025)

Overview of leakage scenarios in supervised machine learning. - Leonard Sasse; Eliana Nicolaisen-Sobesky; Juergen Dukart; Simon B. Eickhoff; Michael Götz; Sami Hamdan; Vera Komeyer; Abhijit Kulkarni; Juha Lahnakoski; Bradley C. Love; Federico Raimondo; Kaustubh R. Patil (J. Big Data, 2025)