Summary of Entropy Bootstrapping For Weakly Supervised Nuclei Detection, by James Willoughby et al.
Entropy Bootstrapping for Weakly Supervised Nuclei Detection
by James Willoughby, Irina Voiculescu
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a weakly supervised approach for microscopy structure segmentation, which can significantly reduce the workload required to annotate cell or nuclei instances. The approach uses individual point labels to estimate an underlying distribution of cell pixels, and then infers full cell masks from this distribution using Mask-RCNN. Compared to training on full ground truth masks, the proposed method achieves a good level of performance despite a 95% reduction in pixel labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us segment cells or nuclei more efficiently by using only point labels instead of drawing entire contours. This can be very helpful for scientists who need to analyze lots of microscopy images. |
Keywords
» Artificial intelligence » Mask » Rcnn » Supervised
