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

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 eight 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, 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 convolutional layer and Citation-KNN. It outperforms not only DCT-MIL models using other settings and the pre-trained AlexNet with fine-tuning by montages of eight lung CT images, but also other state-of-art methods. Deep CNN transferred multiple instance learning is suited for identification of COPD using CT images. It can help finding subgroups with high risk of COPD from large populations through CT scans ordered doing lung cancer screening.

Detection and staging of chronic obstructive pulmonary disease using a computed tomography–based weakly supervised deep learning approach

“…2 and consists of three primary steps: (1) preparation of CT lung instances and bags; (2) feature extraction using a ResNet18; and (3) an attention mechanism-based classi er for COPD detection. Whole CT volumes were divided into multiple parts, with a single axial slice (one instance) being selected from each set and formed into a bag (collection of instances) with de ned patient labels (COPD vs Non-COPD) used for training the network (29). A weakly supervised approach, multiple instance learning (MIL), was adopted due to the heterogeneous nature of the COPD CT instances (30).…”

A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects

“…A deep CNN better represents these abnormalities from 3D PRM images than from 2D PRM images; in the former case, the classification accuracy of COPD versus non-COPD reached 89.3%. To our knowledge, our method identifies COPD patients at least as accurately as previous classification approaches [17][18][19]27 . A strong positive correlation was found between some combinations of PRM phenotypes and 3D CNNs.…”

“…Xu et al 27 1 26 . For a fair comparison, we converted these 2D models into the 3D domain and trained them on the same input dataset.…”

Early detection of COPD based on graph convolutional network and small and weakly labeled data

“…This preprocessing method may miss potentially valuable information to a certain extent. For instance, Xu et al used deep CNN to extract the automatically learned features, which are expected to be more discriminative and diverse than these texture features, and achieved an accuracy of 99.29% and an AUC of 0.9826 by transferring MIL for COPD identification [ 43 ]. We believe that if we can use CNN to extract features from raw images directly and then use GCN for classification, the performance of the model will be further improved.…”