A novel automation-assisted cervical cancer reading method based on convolutional neural network

• Autorzy: Xiang Yao , Sun Wanxin , Pan Changli , Yan Meng , Yin Zhihua , Liang Yixiong

Identyfikatory

DOI

10.1016/j.bbe.2020.01.016

• Języki publikacji: EN

Abstrakty

EN

While automation-assisted reading system can improve efficiency, their performance often relies on the success of accurate cell segmentation and hand-craft feature extrac- tion. This paper presents an efficient and totally segmentation-free method for automat-ed cervical cell screening that utilizes modern object detector to directly detect cervical cells or clumps, without the design of specific hand-crafted feature. Specifically, we use the state-of-the-art CNN-based object detection methods, YOLOv3, as our baseline model. In order to improve the classification performance of hard examples which are four highly similar categories, we cascade an additional task-specific classifier. We also investigate the presence of unreliable annotations and coped with them by smoothing the distribu- tion of noisy labels. We comprehensively evaluate our methods on our test set which is consisted of 1014 annotated cervical cell images with size of 4000 3000 and complex cellular situation corresponding to 10 categories. Our model achieves 97.5% sensitivity (Sens) and 67.8% specificity (Spec) on cervical cell image-level screening. Moreover, we obtain a best mean average precision (mAP) of 63.4% on cervical cell-level diagnosis, and improve the average precision (AP) of hard examples which are the most valuable but most difficult to distinguish. Our automation-assisted cervical cell reading system not only achieves cervical cell image-level classification but also provides more detailed location and category reference information of abnormal cells. The results indicate feasible performance of our method, together with the efficiency and robustness, provid- ing a new idea for future development of computer-assisted reading systems in clinical cervical screening.

Słowa kluczowe

EN

cervical cancer screening; Papanicolaou test; cervical cytology; convolutional neural network; Bethesda system

PL

rak szyjki macicy; rozmaz Papanicolaou; konwolucyjna sieć neuronowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 2
• Strony: 611--623
• Opis fizyczny: Bibliogr. 68 poz., rys., tab., wykr.

Twórcy

autor

Xiang Yao

• School of Computer Science and Engineering, Central South University, Changsha, China

autor

Sun Wanxin

• School of Computer Science and Engineering, Central South University, Changsha, China

autor

Pan Changli

• School of Computer Science and Engineering, Central South University, Changsha, China

autor

Yan Meng

• School of Computer Science and Engineering, Central South University, Changsha, China

autor

Yin Zhihua

• School of Computer Science and Engineering, Central South University, Changsha, China

autor

Liang Yixiong

• School of Computer Science and Engineering, Central South University, Room 411, Computer Building, Lushan South Road, No 932, Changsha 410083, China

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).