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• Michael Goetz

• 2025
• 
  [j8]
  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:
  Overview of leakage scenarios in supervised machine learning.J. Big Data12(1):135 (2025)
• 
  [j7]
  Luisa Gallée,Catharina Silvia Lisson,Timo Ropinski,Meinrad Beer,Michael Götz:
  Proto-Caps: interpretable medical image classification using prototype learning and privileged information.PeerJ Comput. Sci.11:e2908 (2025)
• 
  [c32]
  Luisa Gallée,Catharina Silvia Lisson,Christoph Gerhard Lisson,Daniela Drees,Felix Weig,Daniel Vogele,Meinrad Beer,Michael Götz:
  Abstract: Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model.Bildverarbeitung für die Medizin2025:75
• 
  [c31]
  Daniel Wolf,Tristan Payer,Catharina Silvia Lisson,Christoph Gerhard Lisson,Meinrad Beer,Michael Götz,Timo Ropinski:
  Abstract: Selective Reduction of CT Data for Self-supervised Pre-training Improves Downstream Classification Performance.Bildverarbeitung für die Medizin2025:284
• 
  [c30]
  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,Klaus H. Maier-Hein:
  Abstract: Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution.Bildverarbeitung für die Medizin2025:348-349
• 
  [c29]
  Daniel Wolf,Heiko Hillenhagen,Billurvan Taskin,Alex Bäuerle,Meinrad Beer,Michael Götz,Timo Ropinski:
  Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images.MICCAI (5)2025:691-701
• 
  [i14]
  Luisa Gallée,Catharina Silvia Lisson,Meinrad Beer,Michael Götz:
  Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification.CoRRabs/2502.08997 (2025)
• 
  [i13]
  Daniel Wolf,Heiko Hillenhagen,Billurvan Taskin,Alex Bäuerle,Meinrad Beer,Michael Götz,Timo Ropinski:
  Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images.CoRRabs/2508.00549 (2025)
• 
  [i12]
  Luisa Gallée,Catharina Silvia Lisson,Christoph Gerhard Lisson,Daniela Drees,Felix Weig,Daniel Vogele,Meinrad Beer,Michael Götz:
  Minimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification.CoRRabs/2508.00639 (2025)
• 
  [i11]
  Luisa Gallée,Yiheng Xiong,Meinrad Beer,Michael Götz:
  FunnyNodules: A Customizable Medical Dataset Tailored for Evaluating Explainable AI.CoRRabs/2511.15481 (2025)
• 2024
• 
  [j6]
  Daniel Wolf,Tristan Payer,Catharina Silvia Lisson,Christoph Gerhard Lisson,Meinrad Beer,Michael Götz,Timo Ropinski:
  Less is More: Selective reduction of CT data for self-supervised pre-training of deep learning models with contrastive learning improves downstream classification performance.Comput. Biol. Medicine183:109242 (2024)
• 
  [c28]
  Luisa Gallée,Meinrad Beer,Michael Götz:
  Abstract: Interpretable Medical Image Classification Using Prototype Learning and Privileged Information.Bildverarbeitung für die Medizin2024:25
• 
  [c27]
  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,Claus Peter Heußel,Tobias Norajitra:
  Abstract: Radiomics Processing Toolkit - Role of Feature Computation on Prediction Performance.Bildverarbeitung für die Medizin2024:129
• 
  [c26]
  Daniel Wolf,Tristan Payer,Catharina Silvia Lisson,Christoph Gerhard Lisson,Meinrad Beer,Michael Götz,Timo Ropinski:
  Abstract: Self-supervised Pre-training for Dealing with Small Datasets in Deep Learning for Medical Imaging - Evaluation of Contrastive and Masked Autoencoder Methods.Bildverarbeitung für die Medizin2024:157
• 
  [c25]
  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:
  Abstract: Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning.Bildverarbeitung für die Medizin2024:158
• 
  [c24]
  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:
  Learned Image Compression for HE-Stained Histopathological Images via Stain Deconvolution.MOVI@MICCAI2024:97-107
• 
  [c23]
  Luisa Gallée,Catharina Silvia Lisson,Christoph Gerhard Lisson,Daniela Drees,Felix Weig,Daniel Vogele,Meinrad Beer,Michael Götz:
  Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model.xAI (1)2024:43-56
• 
  [i10]
  Michael Götz,Christian Weber,Christoph Kolb,Klaus H. Maier-Hein:
  Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion Segmentation.CoRRabs/2403.07428 (2024)
• 
  [i9]
  Michael Götz,Christian Weber,Franciszek Binczyk,Joanna Polanska,Rafal Tarnawski,Barbara Bobek-Billewicz,Ullrich Köthe,Jens Kleesiek,Bram Stieltjes,Klaus H. Maier-Hein:
  DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images.CoRRabs/2403.07434 (2024)
• 
  [i8]
  Katerina Deike-Hofmann,Dorottya Dancs,Daniel Paech,Heinz-Peter Schlemmer,Klaus H. Maier-Hein,Philipp Bäumer,Alexander Radbruch,Michael Götz:
  Pre-examinations Improve Automated Metastases Detection on Cranial MRI.CoRRabs/2403.08280 (2024)
• 
  [i7]
  Luisa Gallée,Catharina Silvia Lisson,Christoph Gerhard Lisson,Daniela Drees,Felix Weig,Daniel Vogele,Meinrad Beer,Michael Götz:
  Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model.CoRRabs/2404.09917 (2024)
• 
  [i6]
  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:
  Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution.CoRRabs/2406.12623 (2024)
• 
  [i5]
  Daniel Wolf,Tristan Payer,Catharina Silvia Lisson,Christoph Gerhard Lisson,Meinrad Beer,Michael Götz,Timo Ropinski:
  Less is More: Selective Reduction of CT Data for Self-Supervised Pre-Training of Deep Learning Models with Contrastive Learning Improves Downstream Classification Performance.CoRRabs/2410.14524 (2024)
• 
  [i4]
  Maximilian Fischer,Peter Neher,Peter J. Schüffler,Sebastian Ziegler,Shuhan Xiao,Robin Peretzke,David A. Clunie,Constantin Ulrich,Michael Baumgartner,Alexander Muckenhuber,Silvia Dias Almeida,Michael Götz,Jens Kleesiek,Marco Nolden,Rickmer Braren,Klaus H. Maier-Hein:
  Unlocking the Potential of Digital Pathology: Novel Baselines for Compression.CoRRabs/2412.13137 (2024)
• 2023
• 
  [c22]
  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:
  Abstract: Deep-learning on Lossily Compressed Pathology Images - Adverse Effects for ImageNet Pre-trained Models.Bildverarbeitung für die Medizin2023:245
• 
  [c21]
  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,Claus Peter Heussel,Tobias Norajitra:
  RPTK: The Role of Feature Computation on Prediction Performance.MTSAIL/LEAF/AI4Treat/MMMI/REMIA@MICCAI2023:113-122
• 
  [c20]
  Luisa Gallée,Meinrad Beer,Michael Götz:
  Interpretable Medical Image Classification Using Prototype Learning and Privileged Information.MICCAI (2)2023:435-445
• 
  [c19]
  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:
  Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning.MLMI@MICCAI (2)2023:427-436
• 
  [i3]
  Daniel Wolf,Tristan Payer,Catharina Silvia Lisson,Christoph Gerhard Lisson,Meinrad Beer,Timo Ropinski,Michael Götz:
  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.CoRRabs/2308.06534 (2023)
• 
  [i2]
  Luisa Gallée,Meinrad Beer,Michael Götz:
  Interpretable Medical Image Classification using Prototype Learning and Privileged Information.CoRRabs/2310.15741 (2023)
• 
  [i1]
  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:
  On Leakage in Machine Learning Pipelines.CoRRabs/2311.04179 (2023)
• 2022
• 
  [c18]
  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:
  DICOM Whole Slide Imaging for Computational Pathology Research in Kaapana and the Joint Imaging Platform.Bildverarbeitung für die Medizin2022:273-278
• 
  [c17]
  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:
  Deep Learning on Lossily Compressed Pathology Images: Adverse Effects for ImageNet Pre-trained Models.MOVI@MICCAI2022:73-83
• 2021
• 
  [j5]
  Atilla Wohllebe,Michael Götz:
  Communities of Practice for Functional Learning in Agile Contexts: Definition Approach and Call for Research.Int. J. Adv. Corp. Learn.14(1):62-69 (2021)
• 
  [c16]
  Michael Götz,Klaus H. Maier-Hein:
  Abstract: Data Augmentation for Information Transfer - Why Controlling for Confounding Effects in Radiomic Studies is Important and How to do it.Bildverarbeitung für die Medizin2021:140

• 2018
• 
  [c15]
  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:
  Abstract: Physiological Parameter Estimation from Multispectral Images Unleashed.Bildverarbeitung für die Medizin2018:36
• 
  [c14]
  Caspar J. Goch,Jasmin Metzger,Martin Hettich,André Klein,Tobias Norajitra,Michael Götz,Jens Petersen,Klaus H. Maier-Hein,Marco Nolden:
  Automated Containerized Medical Image Processing Based on MITK and Python - A Modular System to Implement Medical Image Processing Pipelines and Visualize Meta Data.Bildverarbeitung für die Medizin2018:315
• 2017
• 
  [b1]
  Michael Götz:
  Variability-Aware and Weakly Supervised Learning for Semantic Tissue Segmentation. Karlsruhe Institute of Technology, Germany,2017
• 
  [j4]
  Franciszek Binczyk,Bram Stjelties,Christian Weber,Michael Götz,Klaus H. Maier-Hein,Hans-Peter Meinzer,Barbara Bobek-Billewicz,Rafal Tarnawski,Joanna Polanska:
  MiMSeg - an algorithm for automated detection of tumor tissue on NMR apparent diffusion coefficient maps.Inf. Sci.384:235-248 (2017)
• 
  [j3]
  Oskar Maier,Bjoern H. Menze,Janina von der Gablentz,Levin Häni,Mattias P. Heinrich,Matthias Liebrand,Stefan Winzeck,Abdul Basit,Paul Bentley,Liang Chen,Daan Christiaens,Francis Dutil,Karl Egger,Chaolu Feng,Ben Glocker,Michael Götz,Tom Haeck,Hanna-Leena Halme,Mohammad Havaei,Khan M. Iftekharuddin,Pierre-Marc Jodoin,et al.:
  ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI.Medical Image Anal.35:250-269 (2017)
• 
  [c13]
  Michael Götz,Klaus H. Maier-Hein:
  Training mit positiven und unannotierten Daten für automatische Voxelklassifikation.Bildverarbeitung für die Medizin2017:237-242
• 
  [c12]
  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:
  Physiological Parameter Estimation from Multispectral Images Unleashed.MICCAI (3)2017:134-141
• 2016
• 
  [j2]
  Patrick Philipp,Maria Maleshkova,Darko Katic,Christian Weber,Michael Götz,Achim Rettinger,Stefanie Speidel,Benedikt Kämpgen,Marco Nolden,Anna-Laura Wekerle,Rüdiger Dillmann,Hannes Kenngott,Beat P. Müller-Stich,Rudi Studer:
  Toward cognitive pipelines of medical assistance algorithms.Int. J. Comput. Assist. Radiol. Surg.11(9):1743-1753 (2016)
• 
  [j1]
  Michael Götz,Christian Weber,Franciszek Binczyk,Joanna Polanska,Rafal Tarnawski,Barbara Bobek-Billewicz,Ullrich Köthe,Jens Kleesiek,Bram Stieltjes,Klaus H. Maier-Hein:
  DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images.IEEE Trans. Medical Imaging35(1):184-196 (2016)
• 
  [c11]
  Michael Götz,Christoph Kolb,Christian Weber,Sebastian Regnery,Klaus H. Maier-Hein:
  Fallspezifisches Lernen zur automatischen Läsionssegmentierung in multimodalen MR-Bildern.Bildverarbeitung für die Medizin2016:62-67
• 
  [c10]
  Michael Götz,Stephan Skornitzke,Christian Weber,Franziska Fritz,Philipp Mayer,Marco Koell,Wolfram Stiller,Klaus H. Maier-Hein:
  Machine-learning based comparison of CT-perfusion maps and dual energy CT for pancreatic tumor detection.Computer-Aided Diagnosis2016:97851R
• 
  [c9]
  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:
  Crowd-Algorithm Collaboration for Large-Scale Endoscopic Image Annotation with Confidence.MICCAI (2)2016:616-623
• 
  [c8]
  Michael Götz,Eric Heim,Keno März,Tobias Norajitra,Mohammadreza Hafezi,Nassim Fard,Arianeb Mehrabi,Max Knoll,Christian Weber,Lena Maier-Hein,Klaus H. Maier-Hein:
  A learning-based, fully automatic liver tumor segmentation pipeline based on sparsely annotated training data.Image Processing2016:97841I
• 2015
• 
  [c7]
  Patrick Philipp,Maria Maleshkova,Michael Götz,Christian Weber,Benedikt Kämpgen,Sascha Zelzer,Klaus H. Maier-Hein,Miriam Klauß,Achim Rettinger:
  Automatisierung von Vorverarbeitungsschritten für medizinische Bilddaten mit semantischen Technologien.Bildverarbeitung für die Medizin2015:263-268
• 
  [c6]
  Christian Weber,Michael Götz,Franciszek Binczyk,Joanna Polanska,Rafal Tarnawski,Barbara Bobek-Billewicz,Hans-Peter Meinzer,Bram Stieltjes,Klaus H. Maier-Hein:
  Überwachtes Lernen zur Prädiktion von Tumorwachstum.Bildverarbeitung für die Medizin2015:473-478
• 
  [c5]
  Michael Götz,Christian Weber,Franciszek Binczyk,Joanna Polanska,Rafal Tarnawski,Barbara Bobek-Billewicz,Hans-Peter Meinzer,Bram Stieltjes,Klaus H. Maier-Hein:
  Automatische Tumorsegmentierung mit spärlich annotierter Lernbasis.Bildverarbeitung für die Medizin2015:486-491
• 
  [c4]
  Peter F. Neher,Michael Götz,Tobias Norajitra,Christian Weber,Klaus H. Maier-Hein:
  A Machine Learning Based Approach to Fiber Tractography Using Classifier Voting.MICCAI (1)2015:45-52
• 
  [c3]
  Michael Götz,Christian Weber,Christoph Kolb,Klaus H. Maier-Hein:
  Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion Segmentation.Brainles@MICCAI2015:284-295
• 2014
• 
  [c2]
  Frank M. Weber,Thomas Stehle,Irina Wächter-Stehle,Michael Götz,Jochen Peters,Sabine Mollus,Jan Balzer,Malte Kelm,Jürgen Weese:
  Analysis of Mitral Valve Motion in 4D Transesophageal Echocardiography for Transcatheter Aortic Valve Implantation.STACOM2014:168-176
• 
  [c1]
  Philipp Gemmeke,Maria Maleshkova,Patrick Philipp,Michael Götz,Christian Weber,Benedikt Kämpgen,Marco Nolden,Klaus H. Maier-Hein,Achim Rettinger:
  Using Linked Data and Web APIs for Automating the Pre-processing of Medical Images.COLD2014

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