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Journal Articles
- 2023
[j7]Saskia D. Hiltemann
, Helena Rasche, Simon L. Gladman, Hans-Rudolf Hotz, Delphine Larivière, Daniel J. Blankenberg, Pratik D. Jagtap, Thomas Wollmann, Anthony Bretaudeau, Nadia Goué, Timothy J. Griffin, Coline Royaux, Yvan Le Bras, Subina P. Mehta, Anna Syme, Frederik Coppens, Bert Droesbeke, Nicola Soranzo, Wendi A. Bacon, Fotis E. Psomopoulos, Cristóbal Gallardo-Alba, John Davis, Melanie Christine Föll, Matthias Fahrner, Maria A. Doyle, Beatriz Serrano-Solano, Anne Fouilloux, Peter van Heusden, Wolfgang Maier, Dave Clements, Florian Heyl, The Galaxy Team, Björn A. Grüning, Bérénice Batut:
Galaxy Training: A powerful framework for teaching! PLoS Comput. Biol. 19(1) (2023)- 2021
- [j6]Thomas Wollmann, Karl Rohr:
Deep Consensus Network: Aggregating predictions to improve object detection in microscopy images. Medical Image Anal. 70: 102019 (2021) - [j5]Christian Ritter, Thomas Wollmann, Ji Young Lee, Andrea Imle, Barbara Müller, Oliver T. Fackler, Ralf Bartenschlager, Karl Rohr:
Data fusion and smoothing for probabilistic tracking of viral structures in fluorescence microscopy images. Medical Image Anal. 73: 102168 (2021) - 2019
- [j4]Christian Ritter, Thomas Wollmann, Patrick Bernhard, Manuel Gunkel, Delia M. Braun, Ji Young Lee, Jan Meiners, Ronald Simon, Guido Sauter, Holger Erfle, Karsten Rippe, Ralf Bartenschlager, Karl Rohr:
Hyperparameter optimization for image analysis: application to prostate tissue images and live cell data of virus-infected cells. Int. J. Comput. Assist. Radiol. Surg. 14(11): 1847-1857 (2019) - [j3]Mitko Veta, Yujing J. Heng, Nikolas Stathonikos, Babak Ehteshami Bejnordi, Francisco Beca, Thomas Wollmann, Karl Rohr, Manan A. Shah, Dayong Wang, Mikaël Rousson, Martin Hedlund, David Tellez, Francesco Ciompi, Erwan Zerhouni, David Lanyi, Matheus Palhares Viana, Vassili Kovalev, Vitali Liauchuk, Josien P. W. Pluim:
Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge. Medical Image Anal. 54: 111-121 (2019) - [j2]Thomas Wollmann, Manuel Gunkel, Inn Chung, Holger Erfle, Karsten Rippe, Karl Rohr:
GRUU-Net: Integrated convolutional and gated recurrent neural network for cell segmentation. Medical Image Anal. 56: 68-79 (2019) - 2016
- [j1]Thomas Wollmann, Farhad Abtahi, Aboozar Eghdam, Fernando Seoane, Kaj Lindecrantz, Martin Haag, Sabine Koch:
User-Centred Design and Usability Evaluation of a Heart Rate Variability Biofeedback Game. IEEE Access 4: 5531-5539 (2016)
Conference and Workshop Papers
- 2021
- [c13]Johannes S. Otterbach, Thomas Wollmann:
Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs. GI-Jahrestagung 2021: 1185-1191 - [c12]Deepthi Sreenivasaiah, Johannes S. Otterbach, Thomas Wollmann:
MEAL: Manifold Embedding-based Active Learning. ICCVW 2021: 1029-1037 - 2020
- [c11]Christian Ritter, Thomas Wollmann, Ji Young Lee, Ralf Bartenschlager, Karl Rohr:
Deep Learning Particle Detection for Probabilistic Tracking in Fluorescence Microscopy Images. ISBI 2020: 977-980 - 2019
- [c10]Thomas Wollmann, Patrick Bernhard, Manuel Gunkel, Delia M. Braun, Jan Meiners, Ronald Simon, Guido Sauter, Holger Erfle, Karsten Rippe, Karl Rohr:
Black-Box Hyperparameter Optimization for Nuclei Segmentation in Prostate Tissue Images. Bildverarbeitung für die Medizin 2019: 345-350 - [c9]Thomas Wollmann, Christian Ritter, Jan-Niklas Dohrke, Ji Young Lee, Ralf Bartenschlager, Karl Rohr:
Detnet: Deep Neural Network For Particle Detection In Fluorescence Microscopy Images. ISBI 2019: 517-520 - 2018
- [c8]Thomas Wollmann, Julia Ivanova, Manuel Gunkel, Inn Chung, Holger Erfle, Karsten Rippe, Karl Rohr:
Multi-channel Deep Transfer Learning for Nuclei Segmentation in Glioblastoma Cell Tissue Images. Bildverarbeitung für die Medizin 2018: 316-321 - [c7]D. Baltissen, Thomas Wollmann, Manuel Gunkel, Inn Chung, Holger Erfle, Karsten Rippe, Karl Rohr:
Comparison of segmentation methods for tissue microscopy images of glioblastoma cells. ISBI 2018: 396-399 - [c6]Thomas Wollmann, C. S. Eijkman, Karl Rohr:
Adversarial domain adaptation to improve automatic breast cancer grading in lymph nodes. ISBI 2018: 582-585 - [c5]Roman Spilger, Thomas Wollmann, Yu Qiang, Andrea Imle, Ji Young Lee, Barbara Müller, Oliver T. Fackler, Ralf Bartenschlager, Karl Rohr:
Deep Particle Tracker: Automatic Tracking of Particles in Fluorescence Microscopy Images Using Deep Learning. DLMIA/ML-CDS@MICCAI 2018: 128-136 - 2017
- [c4]Thomas Wollmann, Karl Rohr:
Automatic Grading of Breast Cancer Whole-Slide Histopathology Images. Bildverarbeitung für die Medizin 2017: 249-253 - [c3]Thomas Wollmann, Karl Rohr:
Deep residual Hough voting for mitotic cell detection in histopathology images. ISBI 2017: 341-344 - 2015
- [c2]Nicola Marsden, Thomas Wollmann, Britta Lohmann, Gerrit Meixner:
Formative Evaluation of Smartwatch Exergaming. MuC (Workshopband) 2015: 145-147 - 2014
- [c1]Friedrich Pawelka, Thomas Wollmann, Jakob Stöber, Tommy Vinh Lam:
Erfolgreiches Lernen durch gamifiziertes E-Learning. GI-Jahrestagung 2014: 2353-2364
Informal and Other Publications
- 2021
- [i5]Johannes S. Otterbach, Thomas Wollmann:
Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs. CoRR abs/2105.03669 (2021) - [i4]Samuel von Baußnern, Johannes S. Otterbach, Adrian Loy, Mathieu Salzmann, Thomas Wollmann:
DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows. CoRR abs/2105.14638 (2021) - [i3]Deepthi Sreenivasaiah, Thomas Wollmann:
MEAL: Manifold Embedding-based Active Learning. CoRR abs/2106.11858 (2021) - 2018
- [i2]Mitko Veta, Yujing J. Heng, Nikolas Stathonikos, Babak Ehteshami Bejnordi, Francisco Beca, Thomas Wollmann, Karl Rohr, Manan A. Shah, Dayong Wang, Mikaël Rousson, Martin Hedlund, David Tellez, Francesco Ciompi, Erwan Zerhouni, David Lanyi, Matheus Palhares Viana, Vassili Kovalev, Vitali Liauchuk, Hady Ahmady Phoulady, Talha Qaiser, Simon Graham, Nasir M. Rajpoot, Erik Sjöblom, Jesper Molin, Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Zhipeng Jia, Eric I-Chao Chang, Yan Xu, Andrew H. Beck, Paul J. van Diest, Josien P. W. Pluim:
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge. CoRR abs/1807.08284 (2018) - 2017
- [i1]Thomas Wollmann, Karl Rohr:
Automatic breast cancer grading in lymph nodes using a deep neural network. CoRR abs/1707.07565 (2017)
Coauthor Index
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