Rippel, Oliver

The Liver Tumor Segmentation Benchmark (LiTS)

Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2019) · arXiv

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 1,412

The Liver Tumor Segmentation Benchmark (LiTS)

Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2023) · Medical Image Analysis

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 1,412

The Medical Segmentation Decathlon

Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., et al. (2022) · Nature Communications

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 1,384

The StemCellFactory: A Modular System Integration for Automated Generation and Expansion of Human Induced Pluripotent Stem Cells

Elanzew, A., Nießing, B., Langendoerfer, D., Rippel, O., Piotrowski, T., Schenk, F., Kulik, M., Peitz, M., Breitkreuz, Y., Jung, S., et al. (2020) · Frontiers in Bioengineering and Biotechnology

Pluripotent Stem Cells ResearchMesenchymal stem cell research

Cited by 47

GAN-based Defect Synthesis for Anomaly Detection in Fabrics

Rippel, O., Muller, M., & Merhof, D. (2020) · 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

Industrial Vision Systems and Defect DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 22

Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations

Rippel, O., Chavan, A., Lei, C., & Merhof, D. (2022) · Proceedings of the 2nd International Conference on Image Processing and Vision Engineering

Anomaly Detection Techniques and ApplicationsIndustrial Vision Systems and Defect Detection

Cited by 17