Summary of Improving 3d Few-shot Segmentation with Inference-time Pseudo-labeling, by Mohammad Mozafari et al.
Improving 3D Few-Shot Segmentation with Inference-Time Pseudo-Labeling
by Mohammad Mozafari, Hosein Hasani, Reza Vahidimajd, Mohamadreza Fereydooni, Mahdieh Soleymani Baghshah
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper presents a novel approach to improve few-shot segmentation (FSS) models in medical imaging analysis. Existing FSS methods overlook the potential of the query itself, treating it as unlabeled data. However, this paper shows that by leveraging the intrinsic information of the query sample during inference, performance can be boosted. The authors propose a strategy to efficiently utilize the query sample’s valuable information. First, they use support slices from a reference volume to generate an initial segmentation score for the query slices through a prototypical approach. Then, they apply a confidence-aware pseudo-labeling procedure to transfer the most informative parts of query slices to the support set. The final prediction is performed based on the new expanded support set, enabling more accurate segmentation masks. Experiments demonstrate that this method can effectively improve performance across diverse settings and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make medical imaging analysis better by using a new way to look at pictures. Usually, when we’re trying to figure out what’s in a picture, we use some parts of the picture to help us guess what’s in other parts. But this paper shows that we can also use the whole picture itself to get even more accurate results. The authors came up with a clever way to do this by looking at different parts of the picture and using that information to make a better guess about what’s in other parts. They tested their method on lots of pictures and it worked really well. |
Keywords
» Artificial intelligence » Few shot » Inference
