Deep embedded clustering algorithm for clustering PACS repositories
Creating large datasets of medical radiology images from several sources can be challenging because of the differences in the acquisition and storage standards. One possible way of controlling and/or assessing the image selection process is through medical image clustering. This, however, requires an efficient method for learning latent image representations. In this paper, we tackle the problem of fully-unsupervised clustering of medical images using pixel data only. We test the performance of several contemporary approaches, built on top of a convolutional autoencoder (CAE) - convolutional deep embedded clustering (CDEC) and convolutional improved deep embedded clustering (CIDEC) - and three approaches based on preset feature extraction - histogram of oriented gradients (HOG), local binary pattern (LBP) and principal component analysis (PCA). CDEC and CIDEC are end-to-end clustering solutions, involving simultaneous learning of latent representations and clustering assignments, whereas the remaining approaches rely on k-means clustering from fixed embeddings. We train the models on 30,000 images, and test them using a separate test set consisting of 8,000 images. We sampled the data from the PACS repository archive of the Clinical Hospital Centre Rijeka. For evaluation, we use silhouette score, homogeneity score and normalised mutual information (NMI) on two target parameters, closely associated with commonly occurring DICOM tags - Modality and anatomical region (adjusted BodyPartExamined tag). CIDEC attains an NMI score of 0.473 with respect to anatomical region, and CDEC attains an NMI score of 0.645 with respect to the tag Modality - both outperforming other commonly used feature descriptors.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringImage ClusteringTAGMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of Heart Failure Patients
Determining phenotypes of diseases can have considerable benefits for in-hospital patient care and to drug development. The structure of high dimensional data sets such as electronic health records are often represented …
ClusteringDeep Clustering With Consensus Representations
The field of deep clustering combines deep learning and clustering to learn representations that improve both the learned representation and the performance of the considered clustering method. Most existing deep cluster…
ClusteringClustering EnsembleDeep ClusteringData Stream Clustering: A Review
Number of connected devices is steadily increasing and these devices continuously generate data streams. Real-time processing of data streams is arousing interest despite many challenges. Clustering is one of the most su…
ClusteringOutlier DetectionConvex Programming Based Spectral Clustering
Clustering is a fundamental task in data analysis, and spectral clustering has been recognized as a promising approach to it. Given a graph describing the relationship between data, spectral clustering explores the under…
ClusteringImproved Spectral Clustering via Embedded Label Propagation
Spectral clustering is a key research topic in the field of machine learning and data mining. Most of the existing spectral clustering algorithms are built upon Gaussian Laplacian matrices, which are sensitive to paramet…
Clustering