Detecting Shortcuts in Medical Images -- A Case Study in Chest X-rays
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2022
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909805374865408 |
|---|---|
| author | Jiménez-Sánchez, Amelia Juodelyte, Dovile Chamberlain, Bethany Cheplygina, Veronika |
| author_facet | Jiménez-Sánchez, Amelia Juodelyte, Dovile Chamberlain, Bethany Cheplygina, Veronika |
| contents | The availability of large public datasets and the increased amount of computing power have shifted the interest of the medical community to high-performance algorithms. However, little attention is paid to the quality of the data and their annotations. High performance on benchmark datasets may be reported without considering possible shortcuts or artifacts in the data, besides, models are not tested on subpopulation groups. With this work, we aim to raise awareness about shortcuts problems. We validate previous findings, and present a case study on chest X-rays using two publicly available datasets. We share annotations for a subset of pneumothorax images with drains. We conclude with general recommendations for medical image classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_04279 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Detecting Shortcuts in Medical Images -- A Case Study in Chest X-rays Jiménez-Sánchez, Amelia Juodelyte, Dovile Chamberlain, Bethany Cheplygina, Veronika Computer Vision and Pattern Recognition The availability of large public datasets and the increased amount of computing power have shifted the interest of the medical community to high-performance algorithms. However, little attention is paid to the quality of the data and their annotations. High performance on benchmark datasets may be reported without considering possible shortcuts or artifacts in the data, besides, models are not tested on subpopulation groups. With this work, we aim to raise awareness about shortcuts problems. We validate previous findings, and present a case study on chest X-rays using two publicly available datasets. We share annotations for a subset of pneumothorax images with drains. We conclude with general recommendations for medical image classification. |
| title | Detecting Shortcuts in Medical Images -- A Case Study in Chest X-rays |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2211.04279 |