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This paper proposes a sentiment analysis framework based on ranking learning. The framework utilizes BERT model pre-trained on large-scale corpora to extract text features and has two sub-networks for different sentiment analysis tasks. The first sub-network of the framework consists of multiple fully connected layers and intermediate rectified linear units. The main purpose of this sub-network is to learn the presence or absence of various emotions using the extracted text information, and the supervision signal comes from the cross entropy loss function. The other sub-network is a ListNet. Its main purpose is to learn a distribution that approximates the real distribution of different emotions using the correlation between them. Afterwards the predicted distribution can be used to sort the importance of emotions. The two sub-networks of the framework are trained together and can contribute to each other to avoid the deviation from a single network. The framework proposed in this paper has been tested on multiple datasets and the results have shown the proposed framework’s potential.

Background: Computer-aided diagnosis using deep learning algorithms has been initially applied in the field of mammography, but there is no large-scale clinical application.Methods: This study proposed to develop and verify an artificial intelligence model based on mammography. Firstly, retrospectively collected mammograms from six centers were randomized to a training dataset and a validation dataset for establishing the model. Secondly, the model was tested by comparing 12 radiologists’ performance with and without it. Finally, prospectively multicenter mammograms were diagnosed by radiologists with the model. The detection and diagnostic capabilities were evaluated using the free-response receiver operating characteristic (FROC) curve and ROC curve.Results: The sensitivity of model for detecting lesion after matching was 0.908 for false positive rate of 0.25 in unilateral images. The area under ROC curve (AUC) to distinguish the benign from malignant lesions was 0.855 (95% CI: 0.830, 0.880). The performance of 12 radiologists with the model was higher than that of radiologists alone (AUC: 0.852 vs. 0.808, P = 0.005). The mean reading time of with the model was shorter than that of reading alone (80.18 s vs. 62.28 s, P = 0.03). In prospective application, the sensitivity of detection reached 0.887 at false positive rate of 0.25; the AUC of radiologists with the model was 0.983 (95% CI: 0.978, 0.988), with sensitivity, specificity, PPV, and NPV of 94.36%, 98.07%, 87.76%, and 99.09%, respectively.Conclusions: The artificial intelligence model exhibits high accuracy for detecting and diagnosing breast lesions, improves diagnostic accuracy and saves time.Trial registration: NCT, NCT03708978. Registered 17 April 2018, https://register.clinicaltrials.gov/prs/app/ NCT03708978

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