In 2016, the World Health Organization (WHO) updated the glioma classification by incorporating molecular biology parameters, including low‐grade glioma (LGG). In the new scheme, LGGs have three molecular subtypes: isocitrate dehydrogenase (IDH)‐mutated 1p/19q‐codeleted, IDH‐mutated 1p/19q‐noncodeleted, and IDH‐wild type 1p/19q‐noncodeleted entities. This work proposes a model prediction of LGG molecular subtypes using magnetic resonance imaging (MRI). MR images were segmented and converted into radiomics features, thereby providing predictive information about the brain tumor classification. With 726 raw features obtained from the feature extraction procedure, we developed a hybrid machine learning‐based radiomics by incorporating a genetic algorithm and eXtreme Gradient Boosting (XGBoost) classifier, to ascertain 12 optimal features for tumor classification. To resolve imbalanced data, the synthetic minority oversampling technique (SMOTE) was applied in our study. The XGBoost algorithm outperformed the other algorithms on the training dataset by an accuracy value of 0.885. We continued evaluating the XGBoost model, then achieved an overall accuracy of 0.6905 for the three‐subtype classification of LGGs on an external validation dataset. Our model is among just a few to have resolved the three‐subtype LGG classification challenge with high accuracy compared with previous studies performing similar work.
Giant omphaloceles, especially if they contain liver tissue, remain the greatest challenge to pediatric surgeons for the coverage of the huge defect. Various reconstructive techniques have been described in the literature, each with advantages and disadvantages. Standard treatment has been placement of a Silastic silo to allow gradual return of abdominal organs to the abdomen with its limited space. The worst complication of silo placement is infection of the fascia with disruption of the suture line. When fascial infection occurs, closure of the abdominal wall is very difficult or impossible. In this report, the authors describe their experience in treating 5 patients with giant omphaloceles, between 1999 and 2003, utilizing an abs orbable synthetic mesh (polyglactin 910-Vicryl) for abdominal closure and topical application of povidone-iodine 10/100 solution (Betadine®) to prevent infection. All patients had perfect results with the simple postoperative care, early oral feeding and were discharged after 2 months of hospitalization with complete skin coverage.
Vehicle re-identification is a challenging task due to intra-class variability and inter-class similarity across non-overlapping cameras. To tackle these problems, recently proposed methods require additional annotation to extract more features for false positive image exclusion. In this paper, we propose a model powered by adaptive attention modules that requires fewer label annotations but still out-performs the previous models. We also include a re-ranking method that takes account of the importance of metadata feature embeddings in our paper. The proposed method is evaluated on CVPR AI City Challenge 2020 dataset and achieves mAP of 37.25% in Track 2.
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