Is Two-shot All You Need? A Label-efficient Approach for Video Segmentation in Breast Ultrasound
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916145619009536 |
|---|---|
| author | Zeng, Jiajun Ni, Dong Huang, Ruobing |
| author_facet | Zeng, Jiajun Ni, Dong Huang, Ruobing |
| contents | Breast lesion segmentation from breast ultrasound (BUS) videos could assist in early diagnosis and treatment. Existing video object segmentation (VOS) methods usually require dense annotation, which is often inaccessible for medical datasets. Furthermore, they suffer from accumulative errors and a lack of explicit space-time awareness. In this work, we propose a novel two-shot training paradigm for BUS video segmentation. It not only is able to capture free-range space-time consistency but also utilizes a source-dependent augmentation scheme. This label-efficient learning framework is validated on a challenging in-house BUS video dataset. Results showed that it gained comparable performance to the fully annotated ones given only 1.9% training labels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04921 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Is Two-shot All You Need? A Label-efficient Approach for Video Segmentation in Breast Ultrasound Zeng, Jiajun Ni, Dong Huang, Ruobing Image and Video Processing Computer Vision and Pattern Recognition I.4.6 Breast lesion segmentation from breast ultrasound (BUS) videos could assist in early diagnosis and treatment. Existing video object segmentation (VOS) methods usually require dense annotation, which is often inaccessible for medical datasets. Furthermore, they suffer from accumulative errors and a lack of explicit space-time awareness. In this work, we propose a novel two-shot training paradigm for BUS video segmentation. It not only is able to capture free-range space-time consistency but also utilizes a source-dependent augmentation scheme. This label-efficient learning framework is validated on a challenging in-house BUS video dataset. Results showed that it gained comparable performance to the fully annotated ones given only 1.9% training labels. |
| title | Is Two-shot All You Need? A Label-efficient Approach for Video Segmentation in Breast Ultrasound |
| topic | Image and Video Processing Computer Vision and Pattern Recognition I.4.6 |
| url | https://arxiv.org/abs/2402.04921 |