不完美标注对数字病理学中实例分割与分类的CNN训练及性能的影响
Impact of imperfect annotations on CNN training and performance for instance segmentation and classification in digital pathology
AI总结：
本文研究数字病理学中不完美标注对细胞核检测、分割与分类CNN训练的影响，发现使用小型正确标注验证集和预训练可有效抑制对标注噪声的过拟合并保持模型性能。
摘要：
大量实例（如细胞核）的分割与分类是数字病理学中实现准确诊断的关键任务。然而，由于标注过程的复杂性，用于深度学习方法的高质量数据集往往有限。在本工作中，我们研究了噪声标注对一种最先进的CNN模型在组织病理学图像中联合执行细胞核检测、分割与分类任务的训练和性能的影响。在此背景下，我们考察了确定合适训练轮数以防止在训练过程中对标注噪声过拟合的条件。我们的结果表明，利用一个规模较小但标注正确的验证集对于避免过拟合并在很大程度上维持模型性能具有重要作用。此外，我们的发现强调了预训练的有益作用。
英文摘要：
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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