© 2024 deepmicroscopy.org

Publications

Here you can find our latest publications. We are working on deep learning-based microscopy applications, such as pathology:

2024:

Aubreville, M., Ganz, J., Ammeling, J., Kaltenecker, C., & Bertram, C. A. (2024, July 3). Model-based Cleaning of the QUILT-1M Pathology Dataset for Text-Conditional Image Synthesis. Medical Imaging with Deep Learning. Medical Imaging with Deep Learning, Paris, France. https://openreview.net/forum?id=m7wYKrUjzV Cite
Fick, R. H. J., Bertram, C. A., & Aubreville, M. (2024, July 3). Improving CNN-Based Mitosis Detection through Rescanning Annotated Glass Slides and Atypical Mitosis Subtyping. Medical Imaging with Deep Learning 2024. Medical Imaging with Deep Learning 2024. https://openreview.net/forum?id=00gWBAAbMI Cite
Pernias, P., Rampas, D., Richter, M. L., Pal, C., & Aubreville, M. (2024, May 3). Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models. The Twelfth International Conference on Learning Representations. The Twelfth International Conference on Learning Representations (ICLR), Vienna, Austria. http://arxiv.org/abs/2306.00637 Cite Download
Oetter, N., Pröll, J., Sievert, M., Goncalves, M., Rohde, M., Nobis, C.-P., Knipfer, C., Aubreville, M., Pan, Z., Breininger, K., Maier, A., Kesting, M., & Stelzle, F. (2024). Oral mucosa – an examination map for confocal laser endomicroscopy within the oral cavity: an experimental clinical study. Clinical Oral Investigations, 28(5), 266. https://doi.org/10.1007/s00784-024-05664-9 Cite
Sievert, M., Aubreville, M., Mueller, S. K., Eckstein, M., Breininger, K., Iro, H., & Goncalves, M. (2024). Diagnosis of malignancy in oropharyngeal confocal laser endomicroscopy using GPT 4.0 with vision. European Archives of Oto-Rhino-Laryngology. https://doi.org/10.1007/s00405-024-08476-5 Cite
Aubreville, M., Stathonikos, N., Donovan, T. A., Klopfleisch, R., Ammeling, J., Ganz, J., Wilm, F., Veta, M., Jabari, S., Eckstein, M., Annuscheit, J., Krumnow, C., Bozaba, E., Çayır, S., Gu, H., Chen, X. ‘Anthony,’ Jahanifar, M., Shephard, A., Kondo, S., … Bertram, C. A. (2024). Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge. Medical Image Analysis, 94, 103155. https://doi.org/10.1016/j.media.2024.103155 Cite
Wilm, F., Reimann, M., Taubmann, O., Mühlberg, A., & Breininger, K. (2024). Appearance-based Debiasing of Deep Learning Models in Medical Imaging. In A. Maier, T. M. Deserno, H. Handels, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024 (pp. 19–24). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_9 Cite
Aubreville, M., Pan, Z., Sievert, M., Ammeling, J., Ganz, J., Oetter, N., Stelzle, F., Frenken, A.-K., Breininger, K., & Goncalves, M. (2024). Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors. In A. Maier, T. M. Deserno, H. Handels, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024 (pp. 143–148). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_42 Cite
Ammeling, J., Hecker, M., Ganz, J., Donovan, T. A., Klopfleisch, R., Bertram, C. A., Breininger, K., & Aubreville, M. (2024). Automated Mitotic Index Calculation via Deep Learning and Immunohistochemistry. In A. Maier, T. M. Deserno, H. Handels, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024 (pp. 123–128). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_37 Cite
Ganz, J., Puget, C., Ammeling, J., Parlak, E., Kiupel, M., Bertram, C. A., Breininger, K., Klopfleisch, R., & Aubreville, M. (2024). Assessment of Scanner Domain Shifts in Deep Multiple Instance Learning. In A. Maier, T. M. Deserno, H. Handels, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024 (pp. 137–142). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_41 Cite

2023:

Utz, J., Weise, T., Schlereth, M., Wagner, F., Thies, M., Gu, M., Uderhardt, S., & Breininger, K. (2023). Focus on Content not Noise: Improving Image Generation for Nuclei Segmentation by Suppressing Steganography in CycleGAN. 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 3858–3866. https://doi.org/10.1109/ICCVW60793.2023.00417 Cite Download
Ammeling, J., Manger, C., Kwaka, E., Krügel, S., Uhl, M., Kießig, A., Fritz, A., Ganz, J., Riener, A., Bertram, C. A., Breininger, K., & Aubreville, M. (2023). Appealing but Potentially Biasing – Investigation of the Visual Representation of Segmentation Predictions by AI Recommender Systems for Medical Decision Making. MuC ’23: Mensch Und Computer 2023, 330–335. https://doi.org/10.1145/3603555.3608561 Cite
Fragoso-Garcia, M., Wilm, F., Bertram, C. A., Merz, S., Schmidt, A., Donovan, T., Fuchs-Baumgartinger, A., Bartel, A., Marzahl, C., Diehl, L., Puget, C., Maier, A., Aubreville, M., Breininger, K., & Klopfleisch, R. (2023). Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images. Veterinary Pathology, 03009858231189205. https://doi.org/10.1177/03009858231189205 Cite Download
Hirling, D., Tasnadi, E., Caicedo, J., Caroprese, M. V., Sjögren, R., Aubreville, M., Koos, K., & Horvath, P. (2023). Segmentation metric misinterpretations in bioimage analysis. Nature Methods. https://doi.org/10.1038/s41592-023-01942-8 Cite
Aubreville, M., Wilm, F., Stathonikos, N., Breininger, K., Donovan, T. A., Jabari, S., Veta, M., Ganz, J., Ammeling, J., Van Diest, P. J., Klopfleisch, R., & Bertram, C. A. (2023). A comprehensive multi-domain dataset for mitotic figure detection. Scientific Data, 10(1), 484. https://doi.org/10.1038/s41597-023-02327-4 Cite Download
Ammeling, J., Wilm, F., Ganz, J., Breininger, K., & Aubreville, M. (2023). Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge. In B. Sheng & M. Aubreville (Eds.), Mitosis Domain Generalization and Diabetic Retinopathy Analysis (Vol. 13597, pp. 201–205). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-33658-4_19 Cite
Sheng, B., & Aubreville, M. (Eds.). (2023). Mitosis Domain Generalization and Diabetic Retinopathy Analysis: MICCAI Challenges MIDOG 2022 and DRAC 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18–22, 2022, Proceedings (Vol. 13597). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-33658-4 Cite
Rampas, D., Pernias, P., & Aubreville, M. (2023). A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces (No. arXiv:2211.07292). http://arxiv.org/abs/2211.07292 Cite Download
Wilm, F., Ihling, C., Méhes, G., Terracciano, L., Puget, C., Klopfleisch, R., Schüffler, P., Aubreville, M., Maier, A., Mrowiec, T., & Breininger, K. (2023). Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry. Journal of Pathology Informatics, 14, 100301. https://doi.org/10.1016/j.jpi.2023.100301 Cite
Ammeling, J., Schmidt, L.-H., Ganz, J., Niedermair, T., Brochhausen-Delius, C., Schulz, C., Breininger, K., & Aubreville, M. (2023). Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays. In T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2023 (pp. 220–225). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_48 Cite Download
Aubreville, M., Ganz, J., Ammeling, J., Donovan, T. A., Fick, R. Hj., Breininger, K., & Bertram, C. A. (2023). Deep Learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-free Object Detector. In T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2023 (pp. 189–195). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_40 Cite Download
Ganz, J., Lipnik, K., Ammeling, J., Richter, B., Puget, C., Parlak, E., Diehl, L., Klopfleisch, R., Donovan, T. A., Kiupel, M., Bertram, C. A., Breininger, K., & Aubreville, M. (2023). Deep Learning-based Automatic Assessment of AgNOR-scores in Histopathology Images. In T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2023 (pp. 226–231). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_49 Cite Download
Lausser, L. M., Bertram, C. A., Klopfleisch, R., & Aubreville, M. (2023). Limits of Human Expert Ensembles in Mitosis Multi-expert Ground Truth Generation. In T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2023 (pp. 116–121). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_27 Cite
Wilm, F., Fragoso, M., Bertram, C. A., Stathonikos, N., Öttl, M., Qiu, J., Klopfleisch, R., Maier, A., Breininger, K., & Aubreville, M. (2023). Multi-scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset. In T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2023 (pp. 206–211). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_46 Cite Download
Pan, Z., Breininger, K., Aubreville, M., Stelzle, F., Oetter, N., Maier, A., Mantsopoulos, K., Iro, H., Goncalves, M., & Sievert, M. (2023). Defining a baseline identification of artifacts in confocal laser endomicroscopy in head and neck cancer imaging. American Journal of Otolaryngology, 44(2), 103779. https://doi.org/10.1016/j.amjoto.2022.103779 Cite
Aubreville, M., Stathonikos, N., Bertram, C. A., Klopfleisch, R., Ter Hoeve, N., Ciompi, F., Wilm, F., Marzahl, C., Donovan, T. A., Maier, A., Breen, J., Ravikumar, N., Chung, Y., Park, J., Nateghi, R., Pourakpour, F., Fick, R. H. J., Ben Hadj, S., Jahanifar, M., … Breininger, K. (2023). Mitosis domain generalization in histopathology images — The MIDOG challenge. Medical Image Analysis, 84, 102699. https://doi.org/10.1016/j.media.2022.102699 Cite
Utz, J., Schlereth, M., Qiu, J., Thies, M., Wagner, F., Brahim, O. B., Gu, M., Uderhardt, S., & Breininger, K. (2023). McLabel: A Local Thresholding Tool for Efficient Semi-automatic Labelling of Cells in Fluorescence Microscopy. In T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2023 (pp. 82–87). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_20 Cite
Qiu, J., Wilm, F., Öttl, M., Schlereth, M., Liu, C., Heimann, T., Aubreville, M., & Breininger, K. (2023). Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation. In H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. Syeda-Mahmood, & R. Taylor (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (Vol. 14221, pp. 90–100). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43895-0_9 Cite Download

2022:

Wilm, F., Fragoso, M., Marzahl, C., Qiu, J., Puget, C., Diehl, L., Bertram, C. A., Klopfleisch, R., Maier, A., Breininger, K., & Aubreville, M. (2022). Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset. Scientific Data, 9(1), 588. https://doi.org/10.1038/s41597-022-01692-w Cite Download
Marzahl, C., Hill, J., Stayt, J., Bienzle, D., Welker, L., Wilm, F., Voigt, J., Aubreville, M., Maier, A., Klopfleisch, R., Breininger, K., & Bertram, C. A. (2022). Inter-species cell detection – datasets on pulmonary hemosiderophages in equine, human and feline specimens. Scientific Data, 9(1), 269. https://doi.org/10.1038/s41597-022-01389-0 Cite Download
Sievert, M., Mantsopoulos, K., Mueller, S. K., Rupp, R., Eckstein, M., Stelzle, F., Oetter, N., Maier, A., Aubreville, M., Iro, H., & Goncalves, M. (2022). Validation of a classification and scoring system for the diagnosis of laryngeal and pharyngeal squamous cell carcinomas by confocal laser endomicroscopy. Brazilian Journal of Otorhinolaryngology, 88, S26–S32. https://doi.org/10.1016/j.bjorl.2021.06.002 Cite Download
Sievert, M., Eckstein, M., Mantsopoulos, K., Mueller, S. K., Stelzle, F., Aubreville, M., Oetter, N., Maier, A., Iro, H., & Goncalves, M. (2022). Impact of intraepithelial capillary loops and atypical vessels in confocal laser endomicroscopy for the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma. European Archives of Oto-Rhino-Laryngology, 279(4), 2029–2037. https://doi.org/10.1007/s00405-021-06954-8 Cite Download
Bertram, C. A., Aubreville, M., Donovan, T. A., Bartel, A., Wilm, F., Marzahl, C., Assenmacher, C.-A., Becker, K., Bennett, M., Corner, S., Cossic, B., Denk, D., Dettwiler, M., Gonzalez, B. G., Gurtner, C., Haverkamp, A.-K., Heier, A., Lehmbecker, A., Merz, S., … Klopfleisch, R. (2022). Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy. Veterinary Pathology, 59(2), 211–226. https://doi.org/10.1177/03009858211067478 Cite Download
Ganz, J., Bertram, C. A., Klopfleisch, R., Jabari, S., Breininger, K., & Aubreville, M. (2022). Classification of visibility in multi-stain microscopy images. Medical Imaging with Deep Learning 2022. Cite Download
Öttl, M., Mönius, J., Marzahl, C., Rübner, M., Geppert, C. I., Hartmann, A., Beckmann, M. W., Fasching, P., Maier, A., Erber, R., & Breininger, K. (2022). Superpixel Pre-segmentation of HER2 Slides for Efficient Annotation. In K. Maier-Hein, T. M. Deserno, H. Handels, A. Maier, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2022 (pp. 254–259). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-36932-3_54 Cite Download
Wilm, F., Marzahl, C., Breininger, K., & Aubreville, M. (2022). Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge. In M. Aubreville, D. Zimmerer, & M. Heinrich (Eds.), Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis (Vol. 13166, pp. 5–13). Springer International Publishing. https://doi.org/10.1007/978-3-030-97281-3_1 Cite Download

2021:

Marzahl, C., Aubreville, M., Bertram, C. A., Maier, J., Bergler, C., Kröger, C., Voigt, J., Breininger, K., Klopfleisch, R., & Maier, A. (2021). EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control. Scientific Reports, 11(1), 4343. https://doi.org/10.1038/s41598-021-83827-4 Cite Download
Sievert, M., Stelzle, F., Aubreville, M., Mueller, S. K., Eckstein, M., Oetter, N., Maier, A., Mantsopoulos, K., Iro, H., & Goncalves, M. (2021). Intraoperative free margins assessment of oropharyngeal squamous cell carcinoma with confocal laser endomicroscopy: a pilot study. European Archives of Oto-Rhino-Laryngology, 278(11), 4433–4439. https://doi.org/10.1007/s00405-021-06659-y Cite Download
Theelke, L., Wilm, F., Marzahl, C., Bertram, C. A., Klopfleisch, R., Maier, A., Aubreville, M., & Breininger, K. (2021). Iterative Cross-Scanner Registration for Whole Slide Images. 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 582–590. https://doi.org/10.1109/ICCVW54120.2021.00071 Cite
Meuten, D. J., Moore, F. M., Donovan, T. A., Bertram, C. A., Klopfleisch, R., Foster, R. A., Smedley, R. C., Dark, M. J., Milovancev, M., Stromberg, P., Williams, B. H., Aubreville, M., Avallone, G., Bolfa, P., Cullen, J., Dennis, M. M., Goldschmidt, M., Luong, R., Miller, A. D., … Whitley, D. (2021). International Guidelines for Veterinary Tumor Pathology: A Call to Action. Veterinary Pathology, 58(5), 766–794. https://doi.org/10.1177/03009858211013712 Cite Download
Sievert, M., Oetter, N., Aubreville, M., Stelzle, F., Maier, A., Eckstein, M., Mantsopoulos, K., Gostian, A.-O., Mueller, S. K., Koch, M., Agaimy, A., Iro, H., & Goncalves, M. (2021). Feasibility of intraoperative assessment of safe surgical margins during laryngectomy with confocal laser endomicroscopy: A pilot study. Auris Nasus Larynx, 48(4), 764–769. https://doi.org/10.1016/j.anl.2021.01.005 Cite
Donovan, T. A., Moore, F. M., Bertram, C. A., Luong, R., Bolfa, P., Klopfleisch, R., Tvedten, H., Salas, E. N., Whitley, D. B., Aubreville, M., & Meuten, D. J. (2021). Mitotic Figures—Normal, Atypical, and Imposters: A Guide to Identification. Veterinary Pathology, 58(2), 243–257. https://doi.org/10.1177/0300985820980049 Cite
Ganz, J., Kirsch, T., Hoffmann, L., Bertram, C. A., Hoffmann, C., Maier, A., Breininger, K., Blümcke, I., Jabari, S., & Aubreville, M. (2021). Automatic and explainable grading of meningiomas from histopathology images. 69–80. https://proceedings.mlr.press/v156/ganz21a/ganz21a.pdf Cite Download
Marzahl, C., Wilm, F., Tharun, L., Perner, S., Bertram, C. A., Kröger, C., Voigt, J., Klopfleisch, R., Maier, A., & Aubreville, M. (2021). Robust quad-tree based registration on whole slide images. 181–190. Cite
Wilm, F., Bertram, C. A., Marzahl, C., Bartel, A., Donovan, T. A., Assenmacher, C.-A., Becker, K., Bennett, M., Corner, S., Cossic, B., Denk, D., Dettwiler, M., Gonzalez, B. G., Gurtner, C., Heier, A., Lehmbecker, A., Merz, S., Plog, S., Schmidt, A., … Aubreville, M. (2021). Influence of Inter-Annotator Variability on Automatic Mitotic Figure Assessment. In C. Palm, T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2021 (pp. 241–246). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_56 Cite Download
Marzahl, C., Bertram, C. A., Wilm, F., Voigt, J., Barton, A. K., Klopfleisch, R., Breininger, K., Maier, A., & Aubreville, M. (2021). Cell Detection for Asthma on Partially Annotated Whole Slide Images: Learning to be EXACT. In C. Palm, T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2021 (pp. 147–152). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_36 Cite
Bertram, C. A., Donovan, T. A., Tecilla, M., Bartenschlager, F., Fragoso, M., Wilm, F., Marzahl, C., Breininger, K., Maier, A., Klopfleisch, R., & Aubreville, M. (2021). Dataset on Bi- and Multi-nucleated Tumor Cells in Canine Cutaneous Mast Cell Tumors. In C. Palm, T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2021 (pp. 134–139). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_33 Cite Download

2020:

Aubreville, M., Bertram, C. A., Donovan, T. A., Marzahl, C., Maier, A., & Klopfleisch, R. (2020). A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research. Scientific Data, 7(1), 417. https://doi.org/10.1038/s41597-020-00756-z Cite Download
Aubreville, M., Bertram, C. A., Marzahl, C., Gurtner, C., Dettwiler, M., Schmidt, A., Bartenschlager, F., Merz, S., Fragoso, M., Kershaw, O., Klopfleisch, R., & Maier, A. (2020). Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region. Scientific Reports, 10(1), 16447. https://doi.org/10.1038/s41598-020-73246-2 Cite Download
Marzahl, C., Aubreville, M., Bertram, C. A., Stayt, J., Jasensky, A.-K., Bartenschlager, F., Fragoso-Garcia, M., Barton, A. K., Elsemann, S., Jabari, S., Krauth, J., Madhu, P., Voigt, J., Hill, J., Klopfleisch, R., & Maier, A. (2020). Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides. Scientific Reports, 10(1), 9795. https://doi.org/10.1038/s41598-020-65958-2 Cite Download
Schroter, H., Rosenkranz, T., Escalante-B, A. N., Aubreville, M., & Maier, A. (2020). CLCNET: Deep Learning-Based Noise Reduction for Hearing aids using Complex Linear Coding. ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6949–6953. https://doi.org/10.1109/ICASSP40776.2020.9053563 Cite Download
Bertram, C. A., Aubreville, M., Gurtner, C., Bartel, A., Corner, S. M., Dettwiler, M., Kershaw, O., Noland, E. L., Schmidt, A., Sledge, D. G., Smedley, R. C., Thaiwong, T., Kiupel, M., Maier, A., & Klopfleisch, R. (2020). Computerized Calculation of Mitotic Count Distribution in Canine Cutaneous Mast Cell Tumor Sections: Mitotic Count Is Area Dependent. Veterinary Pathology, 57(2), 214–226. https://doi.org/10.1177/0300985819890686 Cite Download
Bertram, C. A., Aubreville, M., Gurtner, C., Bartel, A., Corner, S. M., Dettwiler, M., Kershaw, O., Noland, E. L., Schmidt, A., Sledge, D. G., Smedley, R. C., Thaiwong, T., Kiupel, M., Maier, A., & Klopfleisch, R. (2020). Mitotic Count in Canine Cutaneous Mast Cell Tumours – Not Accurate but Reproducible. Journal of Comparative Pathology, 174, 143. https://doi.org/10.1016/j.jcpa.2019.10.015 Cite
Breininger, K., Pfister, M., Kowarschik, M., & Maier, A. (2020). Move Over There: One-Click Deformation Correction for Image Fusion During Endovascular Aortic Repair. In A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, & L. Joskowicz (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 (Vol. 12264, pp. 713–723). Springer International Publishing. https://doi.org/10.1007/978-3-030-59719-1_69 Cite
Marzahl, C., Bertram, C. A., Aubreville, M., Petrick, A., Weiler, K., Gläsel, A. C., Fragoso, M., Merz, S., Bartenschlager, F., Hoppe, J., Langenhagen, A., Jasensky, A.-K., Voigt, J., Klopfleisch, R., & Maier, A. (2020). Are Fast Labeling Methods Reliable? A Case Study of Computer-Aided Expert Annotations on Microscopy Slides. In A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, & L. Joskowicz (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 (Vol. 12261, pp. 24–32). Springer International Publishing. https://doi.org/10.1007/978-3-030-59710-8_3 Cite Download
Marzahl, C., Aubreville, M., Bertram, C. A., Gerlach, S., Maier, J., Voigt, J., Hill, J., Klopfleisch, R., & Maier, A. (2020). Is Crowd-Algorithm Collaboration an Advanced Alternative to Crowd-Sourcing on Cytology Slides? In T. Tolxdorff, T. M. Deserno, H. Handels, A. Maier, K. H. Maier-Hein, & C. Palm (Eds.), Bildverarbeitung für die Medizin 2020 (pp. 26–31). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-29267-6_5 Cite
Bertram, C. A., Veta, M., Marzahl, C., Stathonikos, N., Maier, A., Klopfleisch, R., & Aubreville, M. (2020). Are Pathologist-Defined Labels Reproducible? Comparison of the TUPAC16 Mitotic Figure Dataset with an Alternative Set of Labels. In J. Cardoso, H. Van Nguyen, N. Heller, P. Henriques Abreu, I. Isgum, W. Silva, R. Cruz, J. Pereira Amorim, V. Patel, B. Roysam, K. Zhou, S. Jiang, N. Le, K. Luu, R. Sznitman, V. Cheplygina, D. Mateus, E. Trucco, & S. Abbasi (Eds.), Interpretable and Annotation-Efficient Learning for Medical Image Computing (Vol. 12446, pp. 204–213). Springer International Publishing. https://doi.org/10.1007/978-3-030-61166-8_22 Cite Download
Aubreville, M., Bertram, C. A., Jabari, S., Marzahl, C., Klopfleisch, R., & Maier, A. (2020). Inter-Species, Inter-Tissue Domain Adaptation for Mitotic Figure Assessment: Learning New Tricks from Old Dogs. In T. Tolxdorff, T. M. Deserno, H. Handels, A. Maier, K. H. Maier-Hein, & C. Palm (Eds.), Bildverarbeitung für die Medizin 2020 (pp. 1–7). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-29267-6_1 Cite Download

2019:

Bertram, C. A., Aubreville, M., Marzahl, C., Maier, A., & Klopfleisch, R. (2019). A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor. Scientific Data, 6(1), 274. https://doi.org/10.1038/s41597-019-0290-4 Cite Download
Goncalves, M., Aubreville, M., Mueller, S. K., Sievert, M., Maier, A., Iro, H., & Bohr, C. (2019). Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds. Acta Otorhinolaryngologica Italica, 39(6), 389–395. https://doi.org/10.14639/0392-100X-2121 Cite Download
Breininger, K., Hanika, M., Weule, M., Kowarschik, M., Pfister, M., & Maier, A. (2019). Simultaneous reconstruction of multiple stiff wires from a single X-ray projection for endovascular aortic repair. International Journal of Computer Assisted Radiology and Surgery, 14(11), 1891–1899. https://doi.org/10.1007/s11548-019-02052-7 Cite
Aubreville, M., Stoeve, M., Oetter, N., Goncalves, M., Knipfer, C., Neumann, H., Bohr, C., Stelzle, F., & Maier, A. (2019). Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images. International Journal of Computer Assisted Radiology and Surgery, 14(1), 31–42. https://doi.org/10.1007/s11548-018-1836-1 Cite
Marzahl, C., Aubreville, M., Voigt, J., & Maier, A. (2019). Classification of Leukemic B-Lymphoblast Cells from Blood Smear Microscopic Images with an Attention-Based Deep Learning Method and Advanced Augmentation Techniques. In A. Gupta & R. Gupta (Eds.), ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging (pp. 13–22). Springer Singapore. https://doi.org/10.1007/978-981-15-0798-4_2 Cite
Aubreville, M., Goncalves, M., Knipfer, C., Oetter, N., Würfl, T., Neumann, H., Stelzle, F., Bohr, C., & Maier, A. (2019). Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract. In A. Cliquet, S. Wiebe, P. Anderson, G. Saggio, R. Zwiggelaar, H. Gamboa, A. Fred, & S. Bermúdez I Badia (Eds.), Biomedical Engineering Systems and Technologies (Vol. 1024, pp. 67–85). Springer International Publishing. https://doi.org/10.1007/978-3-030-29196-9_4 Cite Download
Aubreville, M., Bertram, C. A., Klopfleisch, R., & Maier, A. (2019). Augmented Mitotic Cell Count Using Field of Interest Proposal. In H. Handels, T. M. Deserno, A. Maier, K. H. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2019 (pp. 321–326). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-25326-4_71 Cite Download
Aubreville, M., Bertram, C., Klopfleisch, R., & Maier, A. (2019). Field of Interest Proposal for Augmented Mitotic Cell Count: Comparison of Two Convolutional Networks: Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies, 30–37. https://doi.org/10.5220/0007365700300037 Cite Download

2018:

Aubreville, M., Ehrensperger, K., Maier, A., Rosenkranz, T., Graf, B., & Puder, H. (2018). Deep Denoising for Hearing Aid Applications. 2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC), 361–365. https://doi.org/10.1109/IWAENC.2018.8521369 Cite Download
Breininger, K., Albarqouni, S., Kurzendorfer, T., Pfister, M., Kowarschik, M., & Maier, A. (2018). Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair. International Journal of Computer Assisted Radiology and Surgery, 13(8), 1221–1231. https://doi.org/10.1007/s11548-018-1779-6 Cite
Breininger, K., Würfl, T., Kurzendorfer, T., Albarqouni, S., Pfister, M., Kowarschik, M., Navab, N., & Maier, A. (2018). Multiple Device Segmentation for Fluoroscopic Imaging Using Multi-task Learning. In D. Stoyanov, Z. Taylor, S. Balocco, R. Sznitman, A. Martel, L. Maier-Hein, L. Duong, G. Zahnd, S. Demirci, S. Albarqouni, S.-L. Lee, S. Moriconi, V. Cheplygina, D. Mateus, E. Trucco, E. Granger, & P. Jannin (Eds.), Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (Vol. 11043, pp. 19–27). Springer International Publishing. https://doi.org/10.1007/978-3-030-01364-6_3 Cite
Fu, W., Breininger, K., Schaffert, R., Ravikumar, N., Würfl, T., Fujimoto, J., Moult, E., & Maier, A. (2018). Frangi-Net. In A. Maier, T. M. Deserno, H. Handels, K. H. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2018 (pp. 341–346). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-56537-7_87 Cite
Stoeve, M., Aubreville, M., Oetter, N., Knipfer, C., Neumann, H., Stelzle, F., & Maier, A. (2018). Motion Artifact Detection in Confocal Laser Endomicroscopy Images. In A. Maier, T. M. Deserno, H. Handels, K. H. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2018 (pp. 328–333). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-56537-7_85 Cite Download
Steffes, L.-M., Aubreville, M., Sesselmann, S., Krenn, V., & Maier, A. (2018). Classification of Polyethylene Particles and the Local CD3+ Lymphocytosis in Histological Slices. In A. Maier, T. M. Deserno, H. Handels, K. H. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2018 (pp. 228–233). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-56537-7_63 Cite
Mualla, F., Aubreville, M., & Maier, A. (2018). Microscopy. In A. Maier, S. Steidl, V. Christlein, & J. Hornegger (Eds.), Medical Imaging Systems (Vol. 11111, pp. 69–90). Springer International Publishing. https://doi.org/10.1007/978-3-319-96520-8_5 Cite
Krappmann, M., Aubreville, M., Maier, A., Bertram, C., & Klopfleisch, R. (2018). Classification of Mitotic Cells: Potentials Beyond the Limits of Small Data Sets. In A. Maier, T. M. Deserno, H. Handels, K. H. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2018 (pp. 245–250). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-56537-7_66 Cite

2017:

Aubreville, M., Knipfer, C., Oetter, N., Jaremenko, C., Rodner, E., Denzler, J., Bohr, C., Neumann, H., Stelzle, F., & Maier, A. (2017). Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning. Scientific Reports, 7(1), 11979. https://doi.org/10.1038/s41598-017-12320-8 Cite Download

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