3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures
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Abstract (arXiv)
This paper analyzes the use of 3D Convolutional Neural Networks for brain tumor segmentation in MR images. We address the problem using three different architectures that combine fine and coarse features to obtain the final segmentation. We compare three different networks that use multi-resolution features in terms of both design and performance and we show that they improve their single-resolution counterparts.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 May 2017.
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Citing papers per year: 2021: 4.
4 papers.
- 2021Deep Learning Based 3D Segmentation: A Surveycited by 30
- 2021Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Datacited by 5
- 2021A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Predictioncited by 1
- 2021Triplet Contrastive Learning for Brain Tumor Classificationcited by 1
- 2021Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data2 Dec 2021
- 2021Triplet Contrastive Learning for Brain Tumor Classification8 Aug 2021
- 2021Deep Learning Based 3D Segmentation: A Survey9 Mar 2021
- 2021A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction26 Jan 2021