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Semantic Textual Similarity (STS) evaluation assesses the degree to which two parts of texts are similar, based on their semantic evaluation. In this paper, we describe three models submitted to STS SemEval 2017. Given two English parts of a text, each of proposed methods outputs the assessment of their semantic similarity.We propose an approach for computing monolingual semantic textual similarity based on an ensemble of three distinct methods. Our model consists of recursive neural network (RNN) text auto-encoders ensemble with supervised a model of vectorized sentences using reduced part of speech (PoS) weighted word embeddings as well as unsupervised a method based on word coverage (TakeLab). Additionally, we enrich our model with additional features that allow disambiguation of ensemble methods based on their efficiency. We have used Multi-Layer Perceptron as an ensemble classifier basing on estimations of trained Gradient Boosting Regressors.Results of our research proves that using such ensemble leads to a higher accuracy due to a fact that each memberalgorithm tends to specialize in particular type of sentences. Simple model based on PoS weighted Word2Vec word embeddings seem to improve performance of more complex RNN based auto-encoders in the ensemble. In the monolingual EnglishEnglish STS subtask our Ensemble based model achieved mean Pearson correlation of .785 compared with human annotators.

Background Peroneal split tears are an underrated cause of ankle pain. While magnetic resonance imaging (MRI) is useful for diagnosis, split tears are challenging to identify. The aim of the study was to investigate the association of peroneus brevis split rupture with abnormalities of the superior peroneal retinaculum (SPR), anterior talofibular ligament (ATFL), calcaneofibular ligament (CFL), joint effusion, morphology of the malleolar groove, presence of the bone marrow oedema and prominent peroneal tuberculum. Methods Ankle MRI cases were assessed by independent observers retrospectively in two groups: one with peroneus brevis split tears (n = 80) and one without (control group, n = 115). Two observers evaluated the soft tissue lesions, and three graded the bone lesions. Fisher’s exact test and Pearson correlation were used for analysis. The Bonferroni-Holm method (B-H) was used to adjust for multiple comparisons. Results Only bone marrow edema in the posterior part of the lateral malleolus was significantly (p < 0.05) more common in the split tear group after applying B-H. SPR total rupture was seen only in the experimental group. No differences in incidence of ATFL and CFL lesions or other SPR lesions were noticed (p < 0.05). Conclusion Bone marrow edema in the posterior part of the lateral malleolus is associated with peroneus split tears on MRI.

Background Prostate cancer is one of the most common cancers worldwide. Currently, convolution neural networks (CNNs) are achieving remarkable success in various computer vision tasks, and in medical imaging research. Various CNN architectures and methodologies have been applied in the field of prostate cancer diagnosis. In this work, we evaluate the impact of the adaptation of a state-of-the-art CNN architecture on domain knowledge related to problems in the diagnosis of prostate cancer. The architecture of the final CNN model was optimised on the basis of the Prostate Imaging Reporting and Data System (PI-RADS) standard, which is currently the best available indicator in the acquisition, interpretation, and reporting of prostate multi-parametric magnetic resonance imaging (mpMRI) examinations. Methods A dataset containing 330 suspicious findings identified using mpMRI was used. Two CNN models were subjected to comparative analysis. Both implement the concept of decision-level fusion for mpMRI data, providing a separate network for each multi-parametric series. The first model implements a simple fusion of multi-parametric features to formulate the final decision. The architecture of the second model reflects the diagnostic pathway of PI-RADS methodology, using information about a lesion’s primary anatomic location within the prostate gland. Both networks were experimentally tuned to successfully classify prostate cancer changes. Results The optimised knowledge-encoded model achieved slightly better classification results compared with the traditional model architecture (AUC = 0.84 vs. AUC = 0.82). We found the proposed model to achieve convergence significantly faster. Conclusions The final knowledge-encoded CNN model provided more stable learning performance and faster convergence to optimal diagnostic accuracy. The results fail to demonstrate that PI-RADS-based modelling of CNN architecture can significantly improve performance of prostate cancer recognition using mpMRI.