Dr. Özdemir Cetin
work +49 6151 16-57 366
Oezdemir Cetin joined the SOS Lab as a visiting professor in February 2018. Subsequently, his project entitled “Rapid segmentation and tracking of yeast cells in microfluidic structures using convolutional neural networks for the measurement of cell fluorescence,” submitted to the AvH Philipp Schwartz Initiative, was accepted in 2020. Extending his project work by shifting it to histopathology images, he continues to work with Frankfurt University Hospital. The updated study mainly concerns the follow-up and counting of cancer cells in histopathology images. Various machine learning techniques are used for this.
His research interests include computational pathology and topics such as bayesian inference, vision transformation, graph networks, multiple-instance learning, and uncertainty in deep learning.
Passionate students who want to work in the computational pathology field can contact me for more information about the topics listed below.
(opens in new tab) The assistant student will help to create a dataset consisting of histopathology images.
|Gul, A. G., Cetin, O., Reich, C., Flinner, N., Prangemeier, T., & Koeppl, H. (2022, April). Histopathological image classification based on self-supervised vision transformer and weak labels. In Medical Imaging 2022: Digital and Computational Pathology(Vol. 12039, pp. 366-373). SPIE.|
|Cetin, O., Shu, Y., Flinner, N., Ziegler, P., Wild, P., & Koeppl, H. (2022, February). Multi-magnification networks for deformable image registration on histopathology images. In 10th International Workshop on Biomedical Image Registration.|
|Reich, C., Prangemeier, T., Cetin, Ö., & Koeppl, H. (2021). OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data. arXiv preprint arXiv:2110.10640.|
|Cetin, O., Seymen, V., & Sakoglu, U. (2020). Multiple sclerosis lesion detection in multimodal MRI using simple clustering-based segmentation and classification. Informatics in Medicine Unlocked, 20, 100409.|
Student Projects (on going)
|Nan Yin||A vision transformer-based Graph neural network for calculating Immune cell score||since 29.08.22|
|Xin Li||Representing Uncertainty in Weakly Supervised Deep Learning for Histopathology Image Segmentation||since 22.08.22|
Student Projects (completed)
|Yankun Wu||Virtual stain transformation of histopathological images based on deep-learning||21.09.2022|
|Mingzhi Chen||Contrastive learning-based stain transformation for cancer grading||03.03.2022|
|Yahia Brini||Image-based molecular subtyping of cancer through deep learning||20.01.2022|
|Yiran Shu||Deep neural network based non-rigid image registration for histopathological images||14.01.2022|
|Ahmet Gökberk Gül||Histopathological Image Classification Based on Self-Supervised Vision Transformer||25.11.2021|
|Rijan Kusatha||Multiple instance learning for cancer molecular subtype classification||03.09.2021|
|Jiajun Deng||Image registration for multi-stained histopathologicalwhole slide images||27.08.2021|
|Berkay Canel||Segmentation of Multiple Sclerosis Lesions on Magnetic Resonance Data of the Human Brain with Convolutional Neural Networks||18.12.2020|
Student Projects (available)