A5078957329- Classical Studies and Legal History
- Scientific Computing and Data Management
- Cell Image Analysis Techniques
- Diverse Legal and Medical Studies
- AI in cancer detection
- European and International Law Studies
- Distributed and Parallel Computing Systems
- Conflict of Laws and Jurisdiction
- Image Retrieval and Classification Techniques
- Video Analysis and Summarization
- Theology and Canon Law Studies
- Chemical Analysis and Environmental Impact
- Cancer-related molecular mechanisms research
- Historical, Literary, and Cultural Studies
- German Literature and Culture Studies
- Law and Political Science
- Historical Legal Studies and Society
- Digital Imaging for Blood Diseases
- Advanced Image and Video Retrieval Techniques
- Electron Spin Resonance Studies
- Data Management and Algorithms
- Machine Learning in Materials Science
- Software Engineering Research
- Inorganic Fluorides and Related Compounds
- Web Data Mining and Analysis
HTW Berlin - University of Applied Sciences
2019-2024
Munich University of Applied Sciences
1901
Building on our success with the Vibro video search system in Video Browser Showdown, we are beginning a new effort by applying technologies to Lifelog Search Challenge for first time. Our approach is treat lifelog data collected given day as frames of continuous clip. While have essentially adopted text-to-image and image-to-image from Vibro, introduced various metadata filters complement capabilities. goal increase efficiency image searches within dataset integrating these improvements....
Training models with semi- or self-supervised learning methods is one way to reduce annotation effort since they rely on unlabeled sparsely labeled datasets. Such approaches are particularly promising for domains a time-consuming process requiring specialized expertise and where high-quality machine datasets scarce, like in computational pathology. Even though some of these have been used the histopathological domain, there is, so far, no comprehensive study comparing different approaches....
Convolutional Neural Networks (CNN) are used for automatic cancer detection in pathological images. These data-driven experiments difficult to reproduce, because the CNNs may require CUDA-enabled Nvidia GPUs acceleration and training is often performed on a large dataset stored researcher's computer, inaccessible others. We introduce RED file format reproducible experiment description, where executable programs packaged referenced as Docker container Data inputs outputs described network...
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