Impact of imperfect annotations on CNN training and performance for instance segmentation and classification in digital pathology
This addresses the problem of limited high-quality datasets for pathologists, but it is incremental as it focuses on optimizing training with existing methods.
The study investigated how noisy annotations affect CNN training and performance for instance segmentation and classification of nuclei in digital pathology, finding that a small, correctly annotated validation set helps prevent overfitting and maintain performance.
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.