mlsanityData to 7 Oct 2026

Medical Image Analysis using Deep Relational Learning

arXiv 2303.16099 ↗First posted 28 Mar 2023cs.CVcited by 0

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Abstract (arXiv)

In the past ten years, with the help of deep learning, especially the rapid development of deep neural networks, medical image analysis has made remarkable progress. However, how to effectively use the relational information between various tissues or organs in medical images is still a very challenging problem, and it has not been fully studied. In this thesis, we propose two novel solutions to this problem based on deep relational learning. First, we propose a context-aware fully convolutional network that effectively models implicit relation information between features to perform medical image segmentation. The network achieves the state-of-the-art segmentation results on the Multi Modal Brain Tumor Segmentation 2017 (BraTS2017) and Multi Modal Brain Tumor Segmentation 2018 (BraTS2018) data sets. Subsequently, we propose a new hierarchical homography estimation network to achieve accurate medical image mosaicing by learning the explicit spatial relationship between adjacent frames. We use the UCL Fetoscopy Placenta dataset to conduct experiments and our hierarchical homography estimation network outperforms the other state-of-the-art mosaicing methods while generating robust and meaningful mosaicing result on unseen frames.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 45 of its 148 references that are in the map. First posted on arXiv 28 Mar 2023.

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45 of 148 references.

References · 45

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33 more
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