Summary of Auxiliary Cyclegan-guidance For Task-aware Domain Translation From Duplex to Monoplex Ihc Images, by Nicolas Brieu et al.
Auxiliary CycleGAN-guidance for Task-Aware Domain Translation from Duplex to Monoplex IHC Images
by Nicolas Brieu, Nicolas Triltsch, Philipp Wortmann, Dominik Winter, Shashank Saran, Marlon Rebelatto, Günter Schmidt
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces a novel approach to translate immunohistochemistry (IHC) assays to chromogenic monoplex images using Generative Adversarial Networks (GANs). The authors propose an alternative constraint leveraging immunofluorescence (IF) images as an auxiliary unpaired image domain, which enables the translation from IHC to monoplex domains. The proposed method is evaluated on a downstream segmentation task and shows improved results compared to baseline approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us learn how to translate one type of image into another using special kinds of artificial intelligence models called Generative Adversarial Networks (GANs). Normally, these models need an “undo” button, but that’s not possible for certain types of images. To fix this, the authors came up with a new way to train GANs by adding extra information from other types of images. This helps us create better translations between IHC and monoplex images. |
Keywords
» Artificial intelligence » Translation
