Zobrazeno 1 - 10
of 507
pro vyhledávání: '"Reischl Markus"'
Publikováno v:
Current Directions in Biomedical Engineering, Vol 9, Iss 1, Pp 335-338 (2023)
U-Net is the go-to approach for biomedical segmentation applications. However, it is not designed to segment overlapping objects, a challenge Mask R-CNN has shown to have great potential in. Yet, Mask R-CNN receives little attention in biomedicine. H
Externí odkaz:
https://doaj.org/article/1c95e857aaa246908336118ccedc7ddc
Publikováno v:
Current Directions in Biomedical Engineering, Vol 9, Iss 1, Pp 190-193 (2023)
We introduce a lightweight framework for semantic segmentation that utilizes structured classifiers as an alternative to deep learning methods. Biomedical data is known for being scarce and difficult to label. However, this framework provides a light
Externí odkaz:
https://doaj.org/article/1e4492dc19a54f64b47bd5e5d9879a66
Publikováno v:
Current Directions in Biomedical Engineering, Vol 9, Iss 1, Pp 467-470 (2023)
Supervised Neural Networks are used for segmentation in many biological and biomedical applications. To omit the time-consuming and tiring process of manual labeling, unsupervised Generative Adversarial Networks (GANs) can be used to synthesize label
Externí odkaz:
https://doaj.org/article/62e4171019184ebd95160caf197d8286
Publikováno v:
Current Directions in Biomedical Engineering, Vol 8, Iss 2, Pp 197-200 (2022)
Deep learning is often used for automated diagnosis support in biomedical image processing scenarios. Annotated datasets are essential for the supervised training of deep neural networks. The problem of consistent and noise-free annotation remains fo
Externí odkaz:
https://doaj.org/article/a568d9279ca74186854406b7c3ef2d03
Publikováno v:
Current Directions in Biomedical Engineering, Vol 8, Iss 2, Pp 329-332 (2022)
Modern medical technology offers potential for the automatic generation of datasets that can be fed into deep learning systems. However, even though raw data for supporting diagnostics can be obtained with manageable effort, generating annotations is
Externí odkaz:
https://doaj.org/article/73dbf4d756404c99af86a62cb12e003e
Publikováno v:
Current Directions in Biomedical Engineering, Vol 8, Iss 2, Pp 305-308 (2022)
3D cell culture models are important tools for the development and testing of new therapeutics. In combination with immunoassays and 3D confocal microscopy, crucial information like morphological or metabolic changes can be examined during drug testi
Externí odkaz:
https://doaj.org/article/7be0b8c3e2734a808e9858ae563c2a6a
Publikováno v:
Journal of Integrative Bioinformatics, Vol 19, Iss 4, Pp 128-44 (2022)
Deep learning models achieve high-quality results in image processing. However, to robustly optimize parameters of deep neural networks, large annotated datasets are needed. Image annotation is often performed manually by experts without a comprehens
Externí odkaz:
https://doaj.org/article/9e5c426941d541ddb3761db86b104779
Autor:
Bruch, Roman, Vitacolonna, Mario, Nürnberg, Elina, Sauer, Simeon, Rudolf, Rüdiger, Reischl, Markus
Biomedical research increasingly relies on 3D cell culture models and AI-based analysis can potentially facilitate a detailed and accurate feature extraction on a single-cell level. However, this requires for a precise segmentation of 3D cell dataset
Externí odkaz:
http://arxiv.org/abs/2408.16471
The assessment of bias within Large Language Models (LLMs) has emerged as a critical concern in the contemporary discourse surrounding Artificial Intelligence (AI) in the context of their potential impact on societal dynamics. Recognizing and conside
Externí odkaz:
http://arxiv.org/abs/2405.13041
Publikováno v:
Current Directions in Biomedical Engineering, Vol 6, Iss 3, Pp 398-401 (2020)
The analysis of microscopic images from cell cultures plays an important role in the development of drugs. The segmentation of such images is a basic step to extract the viable information on which further evaluation steps are build. Classical image
Externí odkaz:
https://doaj.org/article/e4a5b150923f44b58ccb623f327bec7c