DCT-MIL: deep CNN transferred multiple instance learning for COPD identification using CT images.

DCT-MIL: deep CNN transferred multiple instance learning for COPD identification using CT images. Phys Med Biol. 2020 Apr 01;: Authors: Xu C, Qi S, Feng J, Xia S, Kang Y, Yao Y, Qian W Abstract While many pre-defined computed tomographic (CT) measures have been utilized to characterize chronic obstructive pulmonary disease (COPD), it is still challenging to represent pathological alternations of multiple dimensions and highly spatial heterogeneity. Deep CNN transferred multiple instance learning (DCT-MIL) is proposed to identify COPD via CT images. After the lung is divided into 8 sections along the axial direction, one random axial CT image is taken out from each section as one instance. With one instance as the input, the activations of neural layers of AlexNet trained by natural images are extracted as features. After dimension reduction through principle component analysis (PCA), features of all instances are input into three MIL methods: Citation k-Nearest-Neighbor (Citation-KNN), multiple instance support vector machine, and expectation-maximization diverse density. Moreover, the performance dependence of the resulted models on the depth of the neural layer where activations are extracted and the number of features is investigated. The proposed DCT-MIL achieves an exceptional performance with an accuracy of 99.29% and area under curve of 0.9826 while using 100 principle components of features extracted from the fourth convoluti...
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research