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Assessing the Role of Random Forests in Medical Image Segmentation

2021-03-30 16:47:19
Dennis Hartmann, Dominik Müller, Iñaki Soto-Rey, Frank Kramer

Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2103.16492

PDF

https://arxiv.org/pdf/2103.16492.pdf


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