Summary of Hot-distance: Combining One-hot and Signed Distance Embeddings For Segmentation, by Marwan Zouinkhi et al.
Hot-Distance: Combining One-Hot and Signed Distance Embeddings for Segmentation
by Marwan Zouinkhi, Jeff L. Rhoades, Aubrey V. Weigel
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV); Quantitative Methods (q-bio.QM)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 called Hot-Distance, which combines the strengths of signed boundary distance prediction and one-hot encoding to segment subcellular structures in FIB-SEM images. This method aims to increase the amount of usable training data for model fitting, leveraging the benefits of using more data for machine learning models. By incorporating hot-encoding and signed boundary distance predictions, Hot-Distance offers a flexible and effective way to tackle segmentation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new approach called Hot-Distance that helps with segmenting tiny structures inside cells from special kinds of microscope images. This makes it easier to train computer models for this task by using more training data. It’s like getting more pieces of a puzzle to help the model understand what it should look like. |
Keywords
» Artificial intelligence » Machine learning » One hot
