Imaging Seminar: Vision Foundation Models for Microscopy
Event details
| Date | 30.03.2026 |
| Hour | 17:00 › 18:00 |
| Speaker | Constantin Pape, Georg-August-Universität-Göttingen |
| Location |
TBD
|
| Category | Conferences - Seminars |
| Event Language | English |
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Abstract: Microscopy image analysis is becoming more important with the increasing size of bioimaging datasets
due to continuing improvements spatial and time resolution, field of view, multiplexing, etc.
Deep learning-based methods have advanced the state-of-the-art for different analysis tasks, such as cell segmentation,
classification, and tracking. However, the large number of different tools for different tasks makes it difficult to find
the right solution. Moreover, many current tools suffer from limited generalization capabilities and are thus only
applicable in a narrow context. Hence, changes in the data to analyze, e.g. different imaging settings or specimen,
require model re-training, which is often time-consuming and technically challenging.
Vision foundation models offer a solution to theses challenges by providing a single, powerful model that addresses
several image analysis tasks for diverse data conditions.
In this talk, I will present the work of my group to develop vision foundation models for microscopy, in particular
Segment Anything for Microscopy, which addresses many different segmentation problems in light and electron microscopy
within a single tool. I will also introduce our on-going extensions of this model to address further tasks, such as pixel classification, cell classification, and cell tracking, within a single unified framework.
Bio: Prof. Dr. Constantin Pape has been heading the Computational Cell Analytics research group at the Institute of Computer Science in Georg August University Göttingen since March 2022. He is also a member of the Campus Institute for Data Science (CIDAS). He conducts researches into image processing methods with applications in biology and medicine. Before working at Göttingen, Mr. Pape was a postdoctoral fellow at EMBL Heidelberg. His research interests are machine learning and deep learning, applying these methods primarily in image processing for microscopy. He also works on the processing and visualization of large image data sets. His work focuses on interdisciplinary projects with biologists and medical professionals.
The seminar is followed by an aperitif.
Registration appreciated
Abstract: Microscopy image analysis is becoming more important with the increasing size of bioimaging datasets
due to continuing improvements spatial and time resolution, field of view, multiplexing, etc.
Deep learning-based methods have advanced the state-of-the-art for different analysis tasks, such as cell segmentation,
classification, and tracking. However, the large number of different tools for different tasks makes it difficult to find
the right solution. Moreover, many current tools suffer from limited generalization capabilities and are thus only
applicable in a narrow context. Hence, changes in the data to analyze, e.g. different imaging settings or specimen,
require model re-training, which is often time-consuming and technically challenging.
Vision foundation models offer a solution to theses challenges by providing a single, powerful model that addresses
several image analysis tasks for diverse data conditions.
In this talk, I will present the work of my group to develop vision foundation models for microscopy, in particular
Segment Anything for Microscopy, which addresses many different segmentation problems in light and electron microscopy
within a single tool. I will also introduce our on-going extensions of this model to address further tasks, such as pixel classification, cell classification, and cell tracking, within a single unified framework.
Bio: Prof. Dr. Constantin Pape has been heading the Computational Cell Analytics research group at the Institute of Computer Science in Georg August University Göttingen since March 2022. He is also a member of the Campus Institute for Data Science (CIDAS). He conducts researches into image processing methods with applications in biology and medicine. Before working at Göttingen, Mr. Pape was a postdoctoral fellow at EMBL Heidelberg. His research interests are machine learning and deep learning, applying these methods primarily in image processing for microscopy. He also works on the processing and visualization of large image data sets. His work focuses on interdisciplinary projects with biologists and medical professionals.
The seminar is followed by an aperitif.
Registration appreciated
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