A novel attention-guided convolutional network for the detection of abnormal cervical cells in cervical cancer screening

• Published in: Medical image analysis 2021-10, Vol.73, p.102197-102197, Article 102197
• Main Authors: Cao, Lei, Yang, Jinying, Rong, Zhiwei, Li, Lulu, Xia, Bairong, You, Chong, Lou, Ge, Jiang, Lei, Du, Chun, Meng, Hongxue, Wang, Wenjie, Wang, Meng, Li, Kang, Hou, Yan
• Format: Article
• Language: English
• Subjects: Attention mechanism; Cancer; Cancer screening; Cellular biology; Cervical cancer; Cervix; Clinical knowledge; Convolutional neural network; Cytological image; Cytology; Datasets; Deep learning; Detection; Feature extraction; Medical imaging; Medical screening; Mimicry; Sensitivity
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2021.102197

•A novel attention-guided deep learning method to aid in cervical cancer screening.•Attention module to mimic the way pathologist reading a cervical cytology image in clinical workflow.•Preparing a dataset of around 70,552 cervical cytology images for abnormal cervical cell detection.•Comparing the performance of our method with the other related deep learning methods as well as human pathologists.•Results better than the other deep learning methods and comparable to human pathologists with 10-year experience. [Display omitted] Early detection of abnormal cervical cells in cervical cancer screening increases the chances of timely treatment. But manual detection requires experienced pathologists and is time-consuming and error prone. Previously, some methods have been proposed for automated abnormal cervical cell detection, whose performance yet remained debatable. Here, we develop an attention feature pyramid network (AttFPN) for automatic abnormal cervical cell detection in cervical cytology images to assist pathologists to make a more accurate diagnosis. Our proposed method consists of two main components. First, an attention module mimicking the way pathologists reading a cervical cytology image. It learns what features to emphasize or suppress by refining extracted features effectively. Second, a multi-scale region-based feature fusion network guided by clinical knowledge to fuse the refined features for detecting abnormal cervical cells at different scales. The region proposals in the multi-scale network are designed according to the clinical knowledge about size and shape distribution of real abnormal cervical cells. Our method, trained and validated with 7030 annotated cervical cytology images, performs better than the state of art deep learning-based methods. The overall sensitivity, specificity, accuracy, and AUC of an independent testing dataset with 3970 cervical cytology images is 95.83%, 94.81%, 95.08% and 0.991, respectively, which is comparable to that of an experienced pathologist with 10 years of experience. Besides, we further validated our method on an external dataset with 110 cases and 35,013 images from a different organization, the case-level sensitivity, specificity, accuracy, and AUC is 91.30%, 90.62%, 90.91% and 0.934, respectively. Average diagnostic time of our method is 0.04s per image, which is much quicker than the average time of pathologists (14.83s per image). Thus, our AttFPN is effective and efficient in cervical cancer