RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929462021455872 |
|---|---|
| author | Li, Guang Togo, Ren Ogawa, Takahiro Haseyama, Miki |
| author_facet | Li, Guang Togo, Ren Ogawa, Takahiro Haseyama, Miki |
| contents | In this study, we propose a novel method called region-guided masked image modeling (RGMIM) for learning meaningful representations from X-ray images. Our method adopts a new masking strategy that utilizes organ mask information to identify valid regions for learning more meaningful representations. We conduct quantitative evaluations on an open lung X-ray image dataset as well as masking ratio hyperparameter studies. When using the entire training set, RGMIM outperformed other comparable methods, achieving a 0.962 lung disease detection accuracy. Specifically, RGMIM significantly improved performance in small data volumes, such as 5% and 10% of the training set compared to other methods. RGMIM can mask more valid regions, facilitating the learning of discriminative representations and the subsequent high-accuracy lung disease detection. RGMIM outperforms other state-of-the-art self-supervised learning methods in experiments, particularly when limited training data is used. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_00313 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images Li, Guang Togo, Ren Ogawa, Takahiro Haseyama, Miki Computer Vision and Pattern Recognition Machine Learning Image and Video Processing In this study, we propose a novel method called region-guided masked image modeling (RGMIM) for learning meaningful representations from X-ray images. Our method adopts a new masking strategy that utilizes organ mask information to identify valid regions for learning more meaningful representations. We conduct quantitative evaluations on an open lung X-ray image dataset as well as masking ratio hyperparameter studies. When using the entire training set, RGMIM outperformed other comparable methods, achieving a 0.962 lung disease detection accuracy. Specifically, RGMIM significantly improved performance in small data volumes, such as 5% and 10% of the training set compared to other methods. RGMIM can mask more valid regions, facilitating the learning of discriminative representations and the subsequent high-accuracy lung disease detection. RGMIM outperforms other state-of-the-art self-supervised learning methods in experiments, particularly when limited training data is used. |
| title | RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2211.00313 |