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2020

  • M. Bengs, S. Westermann, N. Gessert, D. Eggert, A. O. H. Gerstner, N. A. Mueller, C. Betz, W. Laffers, A. Schlaefer (2020). Spatio-spectral deep learning methods forin-vivohyperspectral laryngeal cancer detection. accepted. [BibTex]

  • M. Neidhardt, M. Bengs, S. Latus, M. Schlüter, T. Saathoff, A. Schlaefer (2020). Deep Learning for High Speed Optical Coherence Elastography. IEEE International Symposium on Biomedical Imaging accepted. [BibTex]

  • M. Schlüter, L. Glandorf, J. Sprenger, M. Gromniak, M. Neidhardt, T. Saathoff, A. Schlaefer (2020). High-Speed Markerless Tissue Motion Tracking Using Volumetric Optical Coherence Tomography Images. IEEE International Symposium on Biomedical Imaging accepted. [Abstract] [www] [BibTex]

  • N. Gessert, T. Sentker, F. Madesta, R. Schmitz, H. Kniep, I. Baltruschat, R. Werner, A. Schlaefer (2020). Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting. IEEE Transactions on Biomedical Engineering. 67 (2), 495-503. [Abstract] [doi] [www] [BibTex]

  • N. Gessert, A. Schlaefer (2020). Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs. Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges. STACOM@MICCAI 2019. Lecture Notes in Computer Science. 375-383. [arxiv][doi]