Impact of imperfect annotations on CNN training and performance for instance segmentation and classification in digital pathology.
Comput Biol Med
; 177: 108586, 2024 Jul.
Article
em En
| MEDLINE
| ID: mdl-38796882
ABSTRACT
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.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article