Impact of imperfect annotations on CNN training and performance for instance segmentation and classification in digital pathology

TL;DR

This paper studies how imperfect annotations affect CNN training for nuclei segmentation and classification in digital pathology, emphasizing the importance of validation sets and pre-training to mitigate noise effects.

Contribution

It provides insights into managing noisy annotations in deep learning for digital pathology, highlighting strategies to prevent overfitting and improve model robustness.

Findings

Small, accurate validation sets help prevent overfitting.

Pre-training enhances model performance under noisy annotations.

Proper training epoch selection is crucial for noise robustness.

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.