Advances of Prevention and Nursing of Deep Venous Thrombosis after Gynecological Tumor Operation

Emainly includes deep venous thrombosis(DVT)and pulmonary thromboembolism(PTE).DVT is caused by the thrombogenesis of red blood cells,platelets and fibrous protein which obstructs the venous backflow leading to inflam-matory changes in the wall of the vein.PTE is caused by the caducous blood clots of DVT that runs with the blood to the lungs.Because DVT and PTE are the venous thromboembolic disease process in two stages,the prevention of DVT and PTE is very important for preventing the rapid onset of PTE and high mortality rate of the postoperative complications.The changes of female hormones and the blood concentration and lipid metabolism disorders make venous thromboembolism more likely to occur during pregnancy.Once the pulmonary vessels were blocked,the patients’life would be threatened severely.To find a more effective way to prevent postoperative venous thrombosis in gynecology we review the prevention and treatment of deep venous thrombosis after gynecological surgery in this paper.

Task-specific Part Discovery for Fine-grained Few-shot Classification

Localizing discriminative object parts(e.g.,bird head)is crucial for fine-grained classification tasks,especially for the more challenging fine-grained few-shot scenario.Previous work always relies on the learned object parts in a unified manner,where they attend the same object parts(even with common attention weights)for different few-shot episodic tasks.In this paper,we propose that it should adaptively capture the task-specific object parts that require attention for each few-shot task,since the parts that can distinguish different tasks are naturally different.Specifically for a few-shot task,after obtaining part-level deep features,we learn a task-specific part-based dictionary for both aligning and reweighting part features in an episode.Then,part-level categorical prototypes are generated based on the part features of support data,which are later employed by calculating distances to classify query data for evaluation.To retain the discriminative ability of the part-level representations(i.e.,part features and part prototypes),we design an optimal transport solution that also utilizes query data in a transductive way to optimize the aforementioned distance calculation for the final predictions.Extensive experiments on five fine-grained benchmarks show the superiority of our method,especially for the 1-shot setting,gaining 0.12%,8.56%and 5.87%improvements over state-of-the-art methods on CUB,Stanford Dogs,and Stanford Cars,respectively.