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Nuclei detection is a fundamental task for numerous downstream analysis of histopathology images. Usually, it requires a large number of labeled images for fully supervised nuclei detection to achieve optimal performance. However, the process of collecting sufficient and high-quality ground truth labels is extremely labor intensive. To alleviate this problem, in this paper, a novel semi-supervised learning framework is proposed for nuclei detection, which optimizes the detection network with the involvement of unlabeled image reconstruction. Specifically, we reconstruct unlabeled images from their detection maps representing detailed information about individual location of candidate nucleus, which will aid in regularizing the training process of the detection network by encouraging spatial consistency between original and reconstructed images. Moreover, to further facilitate image reconstruction, we adopt an adversarial learning scheme using image and instance level discriminators for the classification of original and reconstructed images t. In this way, the capability of the detection network is successfully enhanced by taking advantage of both labeled and unlabeled images, thus leading to more accurate nuclei detection results. Extensive experiments show that we compare favorably with previous studies in various settings, which highlights the effectiveness of our proposed framework.

Semi-Supervised Nuclei Detection in Histopathology Images via Location-Aware Adversarial Image Reconstruction