Biomedical Analysis
The analysis of large biomedical image datasets remains a significant challenge. Whether dealing with high-resolution whole slide images (WSIs) of tissue samples or extensive volumetric data, processing and extracting meaningful features are still computationally expensive and time-consuming tasks.Another major hurdle is the limited availability of annotated data. Patient datasets are rarely publicly accessible, and expert annotations from medical or biological professionals are costly and labor-intensive.To address these challenges, we explore a range of approaches. Classical computer vision techniques offer viable solutions for certain tasks but often reach their limits in more complex scenarios. Therefore, we also integrate machine learning methods, such as self-supervised learning, to enhance analysis capabilities and reduce dependency on labeled data.Below, you will find a selection of our projects in biomedical imaging using machine learning and computer vision.
Related Publications:
- Thiele, S., Kockwelp, J., Wistuba, J., Kliesch, S., Gromoll, J., & Risse, B. (2025). Investigating Imaging, Annotation and Self-Supervision for the Classification of Continuously Developing Cells in Histological Whole Slide Images. in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
- Ernsting, J., Beeken, P. N., Ogoniak, L., Kockwelp, J., Hahn, T., Busch, A. S., & Risse, B. (2024). Towards Population Scale Testis Volume Segmentation in DIXON MRI. arXiv e-print arXiv:2410.22866.
- Kockwelp, J., Thiele, S., Bartsch, J., Haalck, L., Gromoll, J., Schlatt, S., Exeler, R., Bleckmann, A., Lenz, G., Wolf, S., Steffen, B., Berdel, W. E., Schliemann, C., Risse, B., & Angenendt, L. (2024). Deep learning predicts therapy-relevant genetics in acute myeloid leukemia from Pappenheim-stained bone marrow smears. Blood Advances, 8 (1), 70–79. doi: 10.1182/bloodadvances.2023011076.
- Bauer, N, Beckmann, D, Reinhardt, D, et al. 2024. “Therapy-induced modulation of tumor vasculature and oxygenation in a murine glioblastoma model quantified by deep learning-based feature extraction.” Scientific Reports, № 14 (1): 2034–2034. doi: 10.1038/s41598-024-52268-0.
- Kockwelp, J., Thiele, S., Kockwelp, P., Bartsch, J., Schliemann, C., Angenendt, L., & Risse, B. (2022). Cell Selection-based Data Reduction Pipeline for Whole Slide Image Analysis of Acute Myeloid Leukemia. in IEEE/CVF (ed.), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern RecognitionThe IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) (pp. 1825–1834). doi: 10.1109/CVPRW56347.2022.00199.
- Bobe, S, Beckmann, D, Klump, DM, et al. 2022. “Volumetric imaging reveals VEGF-C-dependent formation of hepatic lymph vessels in mice.” Frontiers in cell and developmental biology, № 10: 949896–949896. doi: 10.3389/fcell.2022.949896.