Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2023
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910402899607552 |
|---|---|
| author | Hryniewska-Guzik, Weronika Kędzierska, Maria Biecek, Przemysław |
| author_facet | Hryniewska-Guzik, Weronika Kędzierska, Maria Biecek, Przemysław |
| contents | Lung cancer and covid-19 have one of the highest morbidity and mortality rates in the world. For physicians, the identification of lesions is difficult in the early stages of the disease and time-consuming. Therefore, multi-task learning is an approach to extracting important features, such as lesions, from small amounts of medical data because it learns to generalize better. We propose a novel multi-task framework for classification, segmentation, reconstruction, and detection. To the best of our knowledge, we are the first ones who added detection to the multi-task solution. Additionally, we checked the possibility of using two different backbones and different loss functions in the segmentation task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_01137 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans Hryniewska-Guzik, Weronika Kędzierska, Maria Biecek, Przemysław Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Lung cancer and covid-19 have one of the highest morbidity and mortality rates in the world. For physicians, the identification of lesions is difficult in the early stages of the disease and time-consuming. Therefore, multi-task learning is an approach to extracting important features, such as lesions, from small amounts of medical data because it learns to generalize better. We propose a novel multi-task framework for classification, segmentation, reconstruction, and detection. To the best of our knowledge, we are the first ones who added detection to the multi-task solution. Additionally, we checked the possibility of using two different backbones and different loss functions in the segmentation task. |
| title | Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2308.01137 |