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Attention-guided multiple instance learning for COPD identification: To combine the intensity and morphology

Wu Yanan, Qi Shouliang, Feng Jie, Chang Runsheng, Pang Haowen, Hou Jie, Li Mengqi, Wang Yingxi, Xia Shuyue, Qian Wei

Biocybernetics and Biomedical Engineering

2023

Vol. 43, no. 3

568--585

Chronic obstructive pulmonary disease (COPD) is a complex and multi-component respiratory disease. Computed tomography (CT) images can characterize lesions in COPD patients, but the image intensity and morphology of lung components have not been fully exploited. Two datasets (Dataset 1 and 2) comprising a total of 561 subjects were obtained from two centers. A multiple instance learning (MIL) method is proposed for COPD identification. First, randomly selected slices (instances) from CT scans and multi-view 2D snapshots of the 3D airway tree and lung field extracted from CT images are acquired. Then, three attention-guided MIL models (slice-CT, snapshot-airway, and snapshot-lung-field models) are trained. In these models, a deep convolution neural network (CNN) is utilized for feature extraction. Finally, the outputs of the above three MIL models are combined using logistic regression to produce the final prediction. For Dataset 1, the accuracy of the slice-CT MIL model with 20 instances was 88.1%. The backbone of VGG-16 outperformed Alexnet, Resnet18, Resnet26, and Mobilenet_v2 in feature extraction. The snapshotairway and snapshot-lung-field MIL models achieved accuracies of 89.4% and 90.0%, respectively. After the three models were combined, the accuracy reached 95.8%. The proposed model outperformed several state-of-the-art methods and afforded an accuracy of 83.1% for the external dataset (Dataset 2). The proposed weakly supervised MIL method is feasible for COPD identification. The effective CNN module and attention-guided MIL pooling module contribute to performance enhancement. The morphology information of the airway and lung field is beneficial for identifying COPD.

Multiple-instance learning with pairwise instance similarity

Yuan L., Liu J., Tang X.

International Journal of Applied Mathematics and Computer Science

2014

Vol. 24, no. 3

567--577

Multiple-Instance Learning (MIL) has attracted much attention of the machine learning community in recent years and many real-world applications have been successfully formulated as MIL problems. Over the past few years, several Instance Selection-based MIL (ISMIL) algorithms have been presented by using the concept of the embedding space. Although they delivered very promising performance, they often require long computation times for instance selection, leading to a low efficiency of the whole learning process. In this paper, we propose a simple and efficient ISMIL algorithm based on the similarity of pairwise instances within a bag. The basic idea is selecting from every training bag a pair of the most similar instances as instance prototypes and then mapping training bags into the embedding space that is constructed from all the instance prototypes. Thus, the MIL problem can be solved with the standard supervised learning techniques, such as support vector machines. Experiments show that the proposed algorithm is more efficient than its competitors and highly comparable with them in terms of classification accuracy. Moreover, the testing of noise sensitivity demonstrates that our MIL algorithm is very robust to labeling noise.

Future directions in Multiple Instance Learning

Chiu D., Gondra I., Xu T.

Journal of Theoretical and Applied Computer Science

2013

Vol. 7, nr 3

29--39

In Multiple Instance Learning, each training sample consists of a set of unlabelled instances. The set as a whole is labeled positive if at least one instance in the set is positive, or negative otherwise. Given such training samples, the goal is to learn either an explicit description of the common positive instance(s) or a bag classifier that can assign labels to bags. Previous research has focused on this standard definition of the problem where instances in a set are independent. This raises a question: if we remove the independence assumption, can we generalize the goal of finding a description of the common instance(s) to that of finding a description of the common pattern(s) among instances? Similarly, can we generate bag classifiers that discriminate based on common pattern(s) among instances instead of just common instance(s)? This question raises many other related questions that have not been yet fully explored in the context of this problem. In this paper we first present a survey of existing methods that work with the standard definition of the problem and then elaborate on the previous question in the hope that researchers will investigate this exciting research direction.