Assessing the role of random forests in medical image segmentation
- Neural networks represent a field of research that can quickly achieve very good results in the field of medical image segmentation using a GPU. A possible way to achieve good results without GPUs are random forests. For this purpose, two random forest approaches were compared with a state-of-the-art deep convolutional neural network. To make the comparison the PhC-C2DH-U373 and the retinal imaging datasets were used. The evaluation showed that the deep convolutional neutral network achieved the best results. However, one of the random forest approaches also achieved a similar high performance. Our results indicate that random forest approaches are a good alternative to deep convolutional neural networks and, thus, allow the usage of medical image segmentation without a GPU.
| Author: | Dennis Hartmann, Dominik MüllerORCiDGND, Iñaki Soto-ReyORCiD, Frank KramerORCiDGND |
|---|---|
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/104626 |
| Parent Title (English): | arXiv |
| Publisher: | arXiv |
| Type: | Preprint |
| Language: | English |
| Year of first Publication: | 2021 |
| Release Date: | 2023/06/12 |
| First Page: | arXiv:2103.16492 |
| DOI: | https://doi.org/10.48550/arXiv.2103.16492 |
| Institutes: | Fakultät für Angewandte Informatik |
| Fakultät für Angewandte Informatik / Institut für Informatik | |
| Medizinische Fakultät | |
| Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für IT-Infrastrukturen für die Translationale Medizinische Forschung | |
| Medizinische Fakultät / Universitätsklinikum | |
| Latest Publications (not yet published in print): | Aktuelle Publikationen (noch nicht gedruckt erschienen) |
