Summary of Self-supervised Modality-agnostic Pre-training Of Swin Transformers, by Abhiroop Talasila et al.
Self-Supervised Modality-Agnostic Pre-Training of Swin Transformers
by Abhiroop Talasila, Maitreya Maity, U. Deva Priyakumar
First submitted to arxiv on: 21 May 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 The paper proposes an unsupervised pre-training approach, dubbed SwinFUSE, which learns from different medical imaging modalities like CT and MRI to enhance downstream performance. The model’s three key advantages include learning complementary feature representations from both modalities, a domain-invariance module that highlights salient input regions, and remarkable generalizability surpassing the confines of tasks it was initially pre-trained on. The paper’s experiments show a modest performance trade-off compared to single-modality models but significant out-performance on out-of-distribution modality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new model called SwinFUSE that can learn from different types of medical images, like CT and MRI. This helps the model become better at doing tasks with these images. The model has three special parts: it learns to combine information from both image types, ignores small differences between them, and does well even when shown new images it hasn’t seen before. The results show that this model is better than others at doing similar tasks. |
Keywords
» Artificial intelligence » Unsupervised
