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

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

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

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

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

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

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

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

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

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

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

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

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

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