Application of T2~*-weighted first-pass perfusion imaging in the diagnosis of breast tumors

Objective:To study the diagnostic value of T2*-weighted first-pass perfusion imaging in breast tumors.Methods: We analyzed the magnetic resonance imaging(MRI)information along with the pathological and immunohistochemistry re- sults.Magnetic resonance imaging was performed in 28 patients with breast tumor.The time to signal intensity curves were generated according to the T2*-weighted first-pass perfusion imaging.The curve’s maximal signal intensity drop rate and maximal signal intensity decrease time were analyzed and compared with the pathological diagnoses after surgery.Results: Malignant breast lesions showed higher maximal signal intensity drop rate(44.69%±17.07 vs.17.22%±7.49,P<0.001) than benign lesions,but there was no significant difference of maximal signal decrease time between those two lesions(23.94 s±4.92 vs.20.02 s±6.83,P>0.05).Conclusion:The T2*-weighted first-pass perfusion imaging has enough sensitivity and specificity in breast tumor diagnosis.

Graph-Based Chinese Word Sense Disambiguation with Multi-Knowledge Integration

Word sense disambiguation(WSD)is a fundamental but significant task in natural language processing,which directly affects the performance of upper applications.However,WSD is very challenging due to the problem of knowledge bottleneck,i.e.,it is hard to acquire abundant disambiguation knowledge,especially in Chinese.To solve this problem,this paper proposes a graph-based Chinese WSD method with multi-knowledge integration.Particularly,a graph model combining various Chinese and English knowledge resources by word sense mapping is designed.Firstly,the content words in a Chinese ambiguous sentence are extracted and mapped to English words with BabelNet.Then,English word similarity is computed based on English word embeddings and knowledge base.Chinese word similarity is evaluated with Chinese word embedding and HowNet,respectively.The weights of the three kinds of word similarity are optimized with simulated annealing algorithm so as to obtain their overall similarities,which are utilized to construct a disambiguation graph.The graph scoring algorithm evaluates the importance of each word sense node and judge the right senses of the ambiguous words.Extensive experimental results on SemEval dataset show that our proposed WSD method significantly outperforms the baselines.

Interval type-2 fuzzy logic based radar task priority assignment method for detecting hypersonic-glide vehicles

A radar task priority assignment method based on interval type-2 fuzzy logic system(IT2 FLS)was designed to solve the problem of resource management for phased-array radar to detect hypersonic-glide vehicles(HGVs).The mathematical model of the radar task and the motion and detection models of HGVs are described in detail.The target threat of an HGV is divided into maneuver,speed,azimuth,and distance threats.In the radar task priority assignment method based on IT2 FLS,the maneuver factor,speed,azimuth difference,distance,and initial priority are input variables.The radar task priority is the output variable.To reduce the number of fuzzy rules and avoid rule explosion,an IT2 FLS with a hierarchical structure was designed.Finally,the feasibility of the task priority assignment method was verified by simulations.Simulation results showed that the method based on IT2 FLS has a higher precise tracking rate,mean initial priority,and target threat degree,and a shorter offset time.