Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.14365 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917808528424960 |
|---|---|
| author | Jiménez, Laura Gálvez Decaestecker, Christine |
| author_facet | Jiménez, Laura Gálvez Decaestecker, Christine |
| contents | Segmentation and classification of large numbers of instances, such as cell nuclei, are crucial tasks in digital pathology for accurate diagnosis. However, the availability of high-quality datasets for deep learning methods is often limited due to the complexity of the annotation process. In this work, we investigate the impact of noisy annotations on the training and performance of a state-of-the-art CNN model for the combined task of detecting, segmenting and classifying nuclei in histopathology images. In this context, we investigate the conditions for determining an appropriate number of training epochs to prevent overfitting to annotation noise during training. Our results indicate that the utilisation of a small, correctly annotated validation set is instrumental in avoiding overfitting and maintaining model performance to a large extent. Additionally, our findings underscore the beneficial role of pre-training. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14365 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Impact of imperfect annotations on CNN training and performance for instance segmentation and classification in digital pathology Jiménez, Laura Gálvez Decaestecker, Christine Computer Vision and Pattern Recognition Segmentation and classification of large numbers of instances, such as cell nuclei, are crucial tasks in digital pathology for accurate diagnosis. However, the availability of high-quality datasets for deep learning methods is often limited due to the complexity of the annotation process. In this work, we investigate the impact of noisy annotations on the training and performance of a state-of-the-art CNN model for the combined task of detecting, segmenting and classifying nuclei in histopathology images. In this context, we investigate the conditions for determining an appropriate number of training epochs to prevent overfitting to annotation noise during training. Our results indicate that the utilisation of a small, correctly annotated validation set is instrumental in avoiding overfitting and maintaining model performance to a large extent. Additionally, our findings underscore the beneficial role of pre-training. |
| title | Impact of imperfect annotations on CNN training and performance for instance segmentation and classification in digital pathology |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.14365 |