Summary of Fidelity-imposed Displacement Editing For the Learn2reg 2024 Shg-bf Challenge, by Jiacheng Wang et al.
Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge
by Jiacheng Wang, Xiang Chen, Renjiu Hu, Rongguang Wang, Min Liu, Yaonan Wang, Jiazheng Wang, Hao Li, Hang Zhang
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); 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 A novel multi-modal registration framework is proposed to align second-harmonic generation (SHG) and bright-field (BF) microscopy images in human breast and pancreatic cancer tissues. This framework integrates batch-wise contrastive learning, feature-based pre-alignment, and instance-level optimization to address large discrepancies between SHG and BF images. The effectiveness of this method is validated through experimental results on the Learn2Reg COMULISglobe SHG-BF Challenge, securing the 1st place on the online leaderboard. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to match two kinds of microscopy images is developed. This helps doctors analyze breast and pancreatic cancer tissues better. The big challenge was that the two types of images didn’t always match up well. To fix this, a special framework is designed using three main parts: learning from batches of images, aligning features first, and then fine-tuning for each image separately. This method works really well and even came in 1st place in an online competition. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Multi modal » Optimization
