Epidemiology, Diagnosis and Treatment of Acute Promyelocytic Leukemia in Children: the Experience in China

Acute promyelocytic leukemia (APL) is the subtype of acute myeloid leukemia characterized by an accumulation of abnormal promyelocytes in bone marrow, a severe bleeding tendency and the presence of the chromosomal translocation t(15;17) or variants. APL, the most fatal type of leukemia two decades ago, is highly curable with current treatment strategies. There is evidence that the incidence of APL varies across ethnic groups and that genetic factors play a role in the etiology of APL. And there are some difference between children and adults in APL.1–3 The limited data of children available in many developing countries suggest that the rate of early mortality is high and that long-term survival is poor. Death from bleeding and infection during chemotherapy, relapse and treatment abandonment are among the main cause of treatment failure in APL children as well in adults.2 The status of children APL treatment in China has not been described in general

SiamVGG: Visual Tracking using Deeper Siamese Networks

Recently, we have seen a rapid development of Deep Neural Network (DNN) based visual tracking solutions. Some trackers combine the DNN-based solutions with Discriminative Correlation Filters (DCF) to extract semantic features and successfully deliver the state-of-the-art tracking accuracy. However, these solutions are highly compute-intensive, which require long processing time, resulting unsecured real-time performance. To deliver both high accuracy and reliable real-time performance, we propose a novel tracker called SiamVGG. It combines a Convolutional Neural Network (CNN) backbone and a cross-correlation operator, and takes advantage of the features from exemplary images for more accurate object tracking. The architecture of SiamVGG is customized from VGG-16, with the parameters shared by both exemplary images and desired input video frames. We demonstrate the proposed SiamVGG on OTB-2013/50/100 and VOT 2015/2016/2017 datasets with the state-of-the-art accuracy while maintaining a decent real-time performance of 50 FPS running on a GTX 1080Ti. Our design can achieve 2% higher Expected Average Overlap (EAO) compared to the ECO and C-COT in VOT2017 Challenge