تقرير
Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data
| العنوان: | Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data |
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| المؤلفون: | Corbetta, Valentina, Beets-Tan, Regina, Silva, Wilson |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Multi-centre colonoscopy images from various medical centres exhibit distinct complicating factors and overlays that impact the image content, contingent on the specific acquisition centre. Existing Deep Segmentation networks struggle to achieve adequate generalizability in such data sets, and the currently available data augmentation methods do not effectively address these sources of data variability. As a solution, we introduce an innovative data augmentation approach centred on interpretability saliency maps, aimed at enhancing the generalizability of Deep Learning models within the realm of multi-centre colonoscopy image segmentation. The proposed augmentation technique demonstrates increased robustness across different segmentation models and domains. Thorough testing on a publicly available multi-centre dataset for polyp detection demonstrates the effectiveness and versatility of our approach, which is observed both in quantitative and qualitative results. The code is publicly available at: https://github.com/nki-radiology/interpretability_augmentation Comment: 10 pages, 4 figures, 1 table, accepted at MICCAI 2023 Workshop on Machine Learning in Medical Imaging (MLMI) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2308.15881 |
| رقم الأكسشن: | edsarx.2308.15881 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |