CycleGAN With an Improved Loss Function for Cell Detection Using Partly Labeled Images

The object detection, which has been widely applied in the biomedical field already, is of real significance but technically challenging. In practice, the object detection accuracy is vulnerable to labeling quality, which is usually not a big headache for simple algorithm or model verification since there are a bunch of ideal public available datasets whose classes and tags are all well-marked. However, in real scenarios, image data is often partially or even incorrectly labeled. Particularly, in cell detection, this becomes a thorny issue since the labelling of the dataset is incomplete and inaccurate. To address this issue, we propose a data-augmentation algorithm that can generate full labeled cell image data from incomplete labeled ones. First of all, we randomly extract the labeled objects from raw cell images, and meanwhile, keep their corresponding position information. Next, we employ the framework of cycle-consistent adversarial network, but significantly distinguished from the original one, to generate fully labeled data including both objects and backgrounds. We conduct extensive experiments on a blood cell classification dataset called BCCD to evaluate our model, and experimental results show that our proposed method can successfully address the weak annotation problem and improve the performance of object detection.