Unmasking Interstitial Lung Diseases: Leveraging Masked Autoencoders for Diagnosis
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912523304828928 |
|---|---|
| author | Dack, Ethan Brigato, Lorenzo Dedousis, Vasilis Gote-Schniering, Janine Cheryl Hoppe, Hanno Exadaktylos, Aristomenis Funke-Chambour, Manuela Geiser, Thomas Christe, Andreas Ebner, Lukas Mougiakakou, Stavroula |
| author_facet | Dack, Ethan Brigato, Lorenzo Dedousis, Vasilis Gote-Schniering, Janine Cheryl Hoppe, Hanno Exadaktylos, Aristomenis Funke-Chambour, Manuela Geiser, Thomas Christe, Andreas Ebner, Lukas Mougiakakou, Stavroula |
| contents | Masked autoencoders (MAEs) have emerged as a powerful approach for pre-training on unlabelled data, capable of learning robust and informative feature representations. This is particularly advantageous in diffused lung disease research, where annotated imaging datasets are scarce. To leverage this, we train an MAE on a curated collection of over 5,000 chest computed tomography (CT) scans, combining in-house data with publicly available scans from related conditions that exhibit similar radiological patterns, such as COVID-19 and bacterial pneumonia. The pretrained MAE is then fine-tuned on a downstream classification task for diffused lung disease diagnosis. Our findings demonstrate that MAEs can effectively extract clinically meaningful features and improve diagnostic performance, even in the absence of large-scale labelled datasets. The code and the models are available here: https://github.com/eedack01/lung_masked_autoencoder. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04429 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unmasking Interstitial Lung Diseases: Leveraging Masked Autoencoders for Diagnosis Dack, Ethan Brigato, Lorenzo Dedousis, Vasilis Gote-Schniering, Janine Cheryl Hoppe, Hanno Exadaktylos, Aristomenis Funke-Chambour, Manuela Geiser, Thomas Christe, Andreas Ebner, Lukas Mougiakakou, Stavroula Image and Video Processing Computer Vision and Pattern Recognition Masked autoencoders (MAEs) have emerged as a powerful approach for pre-training on unlabelled data, capable of learning robust and informative feature representations. This is particularly advantageous in diffused lung disease research, where annotated imaging datasets are scarce. To leverage this, we train an MAE on a curated collection of over 5,000 chest computed tomography (CT) scans, combining in-house data with publicly available scans from related conditions that exhibit similar radiological patterns, such as COVID-19 and bacterial pneumonia. The pretrained MAE is then fine-tuned on a downstream classification task for diffused lung disease diagnosis. Our findings demonstrate that MAEs can effectively extract clinically meaningful features and improve diagnostic performance, even in the absence of large-scale labelled datasets. The code and the models are available here: https://github.com/eedack01/lung_masked_autoencoder. |
| title | Unmasking Interstitial Lung Diseases: Leveraging Masked Autoencoders for Diagnosis |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.04429 |