Two-Stage Lesion Detection Approach Based on Dimension-Decomposition and 3D Context  

作　　者：Jiacheng Jiao Haiwei Pan Chunling Chen Tao Jin Yang Dong Jingyi Chen 

机构地区：[1]the College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China [2]the School of Software,Tsinghua University,Beijng 100084,China

出　　处：《Tsinghua Science and Technology》2022年第1期103-113,共11页清华大学学报（自然科学版（英文版）

基　　金：supported by the National Natural Science Foundation of China (Nos. 62072135, 61672181)。

摘　　要：Lesion detection in Computed Tomography(CT) images is a challenging task in the field of computer-aided diagnosis.An important issue is to locate the area of lesion accurately.As a branch of Convolutional Neural Networks(CNNs),3D Context-Enhanced(3DCE) frameworks are designed to detect lesions on CT scans.The False Positives(FPs) detected in 3DCE frameworks are usually caused by inaccurate region proposals,which slow down the inference time.To solve the above problems,a new method is proposed,a dimension-decomposition region proposal network is integrated into 3DCE framework to improve the location accuracy in lesion detection.Without the restriction of "anchors" on ratios and scales,anchors are decomposed to independent "anchor strings".Anchor segments are dynamically combined in accordance with probability,and anchor strings with different lengths dynamically compose bounding boxes.Experiments show that the accurate region proposals generated by our model promote the sensitivity of FPs and spend less inference time compared with the current methods.

关 键 词：lesion detection Computed Tomography(CT) dimension-decomposition 3D context computer-aided diagnosis 

分 类 号：R730[医药卫生—肿瘤]