With the development of artificial intelligence and image processing technology, more and more intelligent diagnosis technologies are used in cervical cancer screening. Among them, the detection of cervical lesions by thin liquid‐based cytology is the most common method for cervical cancer screening. At present, most cervical cancer detection algorithms use the object detection technology of natural images, and often only minor modifications are made while ignoring the specificity of the complex application scenario of cervical lesions detection in cervical smear images. In this study, the authors combine the domain knowledge of cervical cancer detection and the characteristics of pathological cells to design a network and propose a booster for cervical cancer detection (CCDB). The booster mainly consists of two components: the refinement module and the spatial‐aware module. The characteristics of cancer cells are fully considered in the booster, and the booster is light and transplantable. As far as the authors know, they are the first to design a CCDB according to the characteristics of cervical cancer cells. Compared with baseline (Retinanet), the sensitivity at four false positives per image and average precision of the proposed method are improved by 2.79 and 7.2%, respectively.

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Background: Superpixel segmentation is a powerful preprocessing tool to reduce the complexity of image processing. Traditionally, size uniformity is one of the significant features of superpixels. However, in medical images, in which subjects scale varies greatly and background areas are often flat, size uniformity rarely conforms to the varying content. To obtain the fewest superpixels with retaining important details, the size of superpixel should be chosen carefully. Methods: We propose a scale-adaptive superpixel algorithm relaxing the size-uniformity criterion for medical images, especially pathological images. A new path-based distance measure and superpixel region growing schema allow our algorithm to generate superpixels with different scales according to the complexity of image content, that is smaller (larger) superpixels in color-riching areas (flat areas). Results: The proposed superpixel algorithm can generate superpixels with boundary adherence, insensitive to noise, and with extremely big sizes and extremely small sizes on one image. The number of superpixels is much smaller than size-uniformly superpixel algorithms while retaining more details of images. Conclusion:With the proposed algorithm, the choice of superpixel size is automatic, which frees the user from the predicament of setting suitable superpixel size for a given application. The results on the nuclear dataset show that the proposed superpixel algorithm superior to the respective state-of-the-art algorithms on both quantitative and quantitative comparisons.