Summary of Curriculum Learning For Few-shot Domain Adaptation in Ct-based Airway Tree Segmentation, by Maxime Jacovella et al.
Curriculum Learning for Few-Shot Domain Adaptation in CT-based Airway Tree Segmentation
by Maxime Jacovella, Ali Keshavarzi, Elsa Angelini
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 proposes a novel approach to airway segmentation in chest CT scans by integrating Curriculum Learning (CL) into deep learning networks. The goal is to improve the quality and generalization of segmentation across different cohorts. To achieve this, the authors distribute the training set into batches based on ad-hoc complexity scores derived from CT scans and corresponding ground-truth tree features. The approach is specifically designed for few-shot domain adaptation, which is essential when manual annotation of a full fine-tuning dataset is expensive. The paper reports high performance using CL for both full training (Source domain) and few-shot fine-tuning (Target domain), but also highlights potential drawbacks if traditional scoring functions or scan sequencing are used. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Airway segmentation from chest CT scans is important in medicine, but it’s hard to get right. This paper shows how Curriculum Learning can help make the process better. It uses special scores to divide up the training data and then trains a network to learn from this data. The results show that using this approach works well for both full training and fine-tuning with just a little bit of extra data. However, it’s not perfect – other methods might work better in certain situations. |
Keywords
» Artificial intelligence » Curriculum learning » Deep learning » Domain adaptation » Few shot » Fine tuning » Generalization
