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Cervical cell classification is a crucial technique for automatic screening of cervical cancer. Although deep learning has greatly improved the accuracy of cell classification, the performance still cannot meet the needs of practical applications. To solve this problem, we propose a multi-task feature fusion model that consists of one auxiliary task of manual feature fitting and two main classification tasks. The auxiliary task enhances the main tasks in a manner of low-layer feature fusion. The main tasks, i.e., a 2-class classification task and a 5-class classification task, are learned together to realize their mutual reinforcement and alleviate the influence of unreliable labels. In addition, a label smoothing method based on cell category similarity is designed to bring inter-cell class information into the label. Comparative experimental results with other state-of-the-art models on the HUSTC and SIPaKMeD datasets prove the effectiveness of the proposed method. With a high sensitivity of 99.82% and a specificity of 98.12% for the 2-class classification task on the HUSTC dataset, our method shows potential to reduce cytologist workload.

Purpose To establish effective prognostic nomograms using clinical features and detailed magnetic resonance imaging (MRI) findings for primary tumor and regional lymph nodes after intensity-modulated radiotherapy (IMRT) in patients with nasopharyngeal carcinoma. Method The nomogram for overall survival (OS) was based on a retrospective study of 595 patients who underwent IMRT at Sun Yat-sen University Cancer Center from 2010 to 2012. The predictive accuracy and discriminative ability of our nomogram models were determined by concordance index and calibration curve, and were compared with the nomogram models combining clinical features with tumor-node-metastasis (TNM) classification. The results were validated using bootstrap resampling and a cohort study of 241 patients. The same data cohort was used to predict the progress-free survival (PFS) of nasopharyngeal carcinoma with 3:1 training cohort (N = 558) and validation cohort (N = 278). Results The following factors were assembled into our prognostic survival nomogram models: Age, Epstein–Barr virus DNA copy number before treatment (EBV_DNA_CN), tensor veli palatini (TVP) involvement, musculus pterygoideus lateralis (MPL) involvement, prestyloid space (PS) involvement, prevertebral space (PVS) involvement, base of sphenoid bone (BOSB) involvement, paranasal sinus (PNS) involvement, the laterality of Ⅱ (Ⅱ_laterality), the laterality of retropharyngeal lymph node (RPLN_laterality), nodal grouping (NG), extranodal neoplastic spread (ENS), contrast-enhancing rim (CER) and Nodal_number. The calibration curves showed good agreement between nomogram-predicted and actual survival. Our nomogram models for OS and PFS, provided better results than the nomogram models combining clinical features with TNM classification. Results were further confirmed in the validation set. Conclusion Detailed MRI findings of primary tumor and regional lymph nodes can improve the performance of prognostic nomograms for patients with nasopharyngeal carcinoma.