Summary of Nucleus Subtype Classification Using Inter-modality Learning, by Lucas W. Remedios et al.
Nucleus subtype classification using inter-modality learning
by Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios, Ho Hin Lee, Leon Y. Cai, Thomas Li, Ruining Deng, Can Cui, Jia Li, Qi Liu, Ken S. Lau, Joseph T. Roland, Mary K. Washington, Lori A. Coburn, Keith T. Wilson, Yuankai Huo, Bennett A. Landman
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: None
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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 The proposed paper introduces an innovative method for labeling previously un-labeled cell types on virtual Hematoxylin and eosin (H&E) stains using inter-modality learning. By leveraging multiplexed immunofluorescence (MxIF) histology imaging, the authors identify 14 subclasses of cell types and perform style transfer to synthesize virtual H&E from MxIF images. The higher density labels from MxIF are then transferred to these virtual H&E images, enabling the evaluation of the approach’s efficacy. The results show positive predictive values for helper T and progenitor nuclei on virtual H&E, demonstrating a promising step towards automating annotation in digital pathology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have developed a new way to look at cells using special imaging techniques. They took pictures of cells using two different methods: one that shows the cell’s shape (H&E) and another that shows what kinds of molecules are inside the cell (MxIF). By combining these two types of images, they were able to identify many different types of cells that couldn’t be seen before. This is important because it could help doctors better understand how cells work in the body. |
Keywords
* Artificial intelligence * Style transfer
