Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2511.14558 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913167730278400 |
|---|---|
| author | Bajger, Adam Obdržálek, Jan Kůr, Vojtěch Nenutil, Rudolf Holub, Petr Musil, Vít Brázdil, Tomáš |
| author_facet | Bajger, Adam Obdržálek, Jan Kůr, Vojtěch Nenutil, Rudolf Holub, Petr Musil, Vít Brázdil, Tomáš |
| contents | We present a clustering-based explainability technique for digital pathology models based on convolutional neural networks. Unlike commonly used methods based on saliency maps, such as occlusion, GradCAM, or relevance propagation, which highlight regions that contribute the most to the prediction for a single slide, our method shows the global behaviour of the model under consideration, while also providing more fine-grained information. The result clusters can be visualised not only to understand the model, but also to increase confidence in its operation, leading to faster adoption in clinical practice. We also evaluate the performance of our technique on an existing model for detecting prostate cancer, demonstrating its usefulness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14558 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explaining Digital Pathology Models via Clustering Activations Bajger, Adam Obdržálek, Jan Kůr, Vojtěch Nenutil, Rudolf Holub, Petr Musil, Vít Brázdil, Tomáš Computer Vision and Pattern Recognition We present a clustering-based explainability technique for digital pathology models based on convolutional neural networks. Unlike commonly used methods based on saliency maps, such as occlusion, GradCAM, or relevance propagation, which highlight regions that contribute the most to the prediction for a single slide, our method shows the global behaviour of the model under consideration, while also providing more fine-grained information. The result clusters can be visualised not only to understand the model, but also to increase confidence in its operation, leading to faster adoption in clinical practice. We also evaluate the performance of our technique on an existing model for detecting prostate cancer, demonstrating its usefulness. |
| title | Explaining Digital Pathology Models via Clustering Activations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.14558 |