Summary of From Tissue Plane to Organ World: a Benchmark Dataset For Multimodal Biomedical Image Registration Using Deep Co-attention Networks, by Yifeng Wang et al.
From Tissue Plane to Organ World: A Benchmark Dataset for Multimodal Biomedical Image Registration using Deep Co-Attention Networks
by Yifeng Wang, Weipeng Li, Thomas Pearce, Haohan Wang
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers develop a deep learning-based approach to address the challenge of histology-to-organ registration in neuroimaging. The goal is to correlate neuropathology with neuroimaging findings and gain insights into disease states. To achieve this, they create a benchmark dataset called ATOM, sourced from various institutions, which aims to transform the challenge into a machine learning problem. The RegisMCAN model demonstrates impressive performance in predicting where subregions extracted from organ images were obtained within the 3D volume. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses deep learning models to solve the problem of histology-to-organ registration in neuroimaging. The goal is to understand disease states by correlating neuropathology with neuroimaging findings. To do this, researchers create a dataset called ATOM that can be used to train machines to predict where a subregion came from within an organ. |
Keywords
* Artificial intelligence * Deep learning * Machine learning
