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Congress: ECR24
Poster Number: C-14978
Type: EPOS Radiologist (scientific)
DOI: 10.26044/ecr2024/C-14978
Authorblock: K. Geißler1, N. Uchiyama2, R. Grimm3, T. Suzuki4, U. Fischer5, D. H. M. Szolar6, H. Meine1, H. Von Busch3, R. M. Murakami4; 1Bremen/DE, 2TOKYO/JP, 3Erlangen/DE, 4Tokyo/JP, 5Goettingen/DE, 6Graz/AT
Disclosures:
Kai Geißler: Nothing to disclose
Nachiko Uchiyama: Nothing to disclose
Robert Grimm: Employee: Siemens Healthcare GmbH
Takashi Suzuki: Employee: Siemens Healthcare GmbH
Uwe Fischer: Nothing to disclose
Dieter H. M. Szolar: Nothing to disclose
Hans Meine: Nothing to disclose
Heinrich Von Busch: Employee: Siemens Healthcare GmbH
Ryusuke Medical Murakami: Nothing to disclose
Keywords: Artificial Intelligence, Breast, MR, Computer Applications-Detection, diagnosis, Segmentation, Cancer
References

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[2] Lee, M. V., Aharon, S., Kim, K., Sunn Konstantinoff, K., Appleton, C. M., Stwalley, D., & Olsen, M. A. (2022). Recent trends in screening breast MRI. Journal of Breast Imaging, 4(1), 39-47. 

[3] Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016). 3D U-Net: learning dense volumetric segmentation from sparse annotation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19 (pp. 424-432). Springer International Publishing. 

[4] Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 (pp. 234-241). Springer International Publishing. 

[5] Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211. 

[6] Homeyer, A., Schwier, M., & Hahn, H. K. (2010, May). A generic concept for object-based image analysis. In International Conference on Computer Vision Theory and Applications (Vol. 2, pp. 530-533). SCITEPRESS. 
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