Abstract
Automated detection of cervical abnormal cells from Thin-prep cytologic test (TCT) images is essential for efficient cervical abnormal screening by computer-aided diagnosis system. However, the detection performance is influenced by noise samples in the training dataset, mainly due to the subjective differences among cytologists in annotating the training samples. Besides, existing detection methods often neglect visual feature correlation information between cells, which can also be utilized to aid the detection model. In this paper, we propose a cervical abnormal cell detection method optimized by a novel distillation strategy based on local-scale consistency refinement. Firstly, we use a vanilla RetinaNet to detect top-K suspicious cells and extract region-of-interest (ROI) features. Then, a pre-trained Patch Correction Network (PCN) is leveraged to obtain local-scale features and conduct further refinement for these suspicious cell patches. We design a classification ranking loss to utilize refined scores for reducing the effects of the noisy label. Furthermore, the proposed ROI-correlation consistency loss is computed between extracted ROI features and local-scale features to exploit correlation information and optimize RetinaNet. Our experiments demonstrate that our distillation method can greatly optimize the performance of cervical abnormal cell detection without changing the detector’s network structure in the inference. The code is publicly available at https://github.com/feimanman/Cervical-Abnormal-Cell-Detection.
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This work was supported by the National Natural Science Foundation of China (No. 62001292).
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Fei, M. et al. (2023). Robust Cervical Abnormal Cell Detection via Distillation from Local-Scale Consistency Refinement. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_63
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