Reducing the Effect of Incomplete Annotations in Object Detection for Histopathology

Abstract

Histopathology is a crucial component of clinical practice involving microscopic tissue examination. Typically, pathologists manually analyse tissue to locate and label structural units, cells, and organoids. The properties and quantity of these objects can indicate a patient’s condition, e.g., the presence of tumours. Recent advancements in artificial intelligence (AI) have created the potential to automate this process. However, AI methods either provide limited accuracy or require a lot of densely annotated data, which is prohibitively time-consuming and expensive in the histopathology domain due to high object density and labelling difficulty. In this study, we address the challenge of training object detection neural networks on histology data with incomplete annotations. We demonstrate that hyperparameter tuning can mitigate the negative effects of sparsely labelled data. Additionally, we propose a novel model component called the Generalised Background Recalibration Loss to further improve detection rates. It can be adapted to a broader class of object detection models than previous solutions. Our results should facilitate the development of object detection neural networks for histology images by demonstrating the efficient use of sparsely labelled data. Our method reduces the impact of missing annotations on detection rates and thereby eases the most time-consuming aspect of data preparation for neural network training.