Unsupervised Joint Mining of Deep Features and Image Labels for Large-Scale Radiology Image Categorization and Scene Recognition

By Xiaosong Wang, Le Lu, Hoo-Chang Shin, Lauren Kim, Mohammadhadi Bagheri, Isabella Nogues, Jianhua Yao, Ronald M. Summers. 2017

41 citations recorded by the source corpus.

The recent rapid and tremendous success of deep convolutional neural networks (CNN) on many challenging computer vision tasks largely derives from the accessibility of the well-annotated ImageNet and PASCAL VOC datasets. Nevertheless, unsupervised image categorization (i.e., without the ground-truth labeling) is much less investigated, yet critically important and difficult when annotations are extremely hard to obtain in the conventional way of "Google Search" and crowd sourcing. We address this problem by presenting a looped deep pseudo-task optimization (LDPO) framework for joint mining of deep CNN features and image labels. Our method is conceptually simple and rests upon the hypothesized "convergence" of better labels leading to better trained CNN models which in turn feed more discriminative image representations to facilitate more meaningful clusters/labels. Our proposed method is validated in tackling two important applications: 1) Large-scale medical image annotation has always been a prohibitively expensive and easily-biased task even for well-trained radiologists. Significantly better image categorization results are achieved via our proposed approach compared to the previous state-of-the-art method. 2) Unsupervised scene recognition on representative and publicly available datasets with our proposed technique is examined. The LDPO achieves excellent quantitative scene classification results. On the MIT indoor scene dataset, it attains a clustering accuracy of 75:3%, compared to the state-of-the-art supervised classification accuracy of 81:0% (when both are based on the VGG-VD model).

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Deep learning in medical imaging and radiation therapy. (2018, background): “Wang et al. 327 exploited a combination of image features and textual information extracted from reports to label groups of images to alleviate these limitations.”
Leveraging Large-Scale Uncurated Data for Unsupervised Pre-training of Visual Features (2019, background): “Various attempts have been made to train convnets using clustering [2, 3, 6, 29, 48, 52, 53].”
Weakly supervised classification of rare aortic valve malformations using unlabeled cardiac MRI sequences (2018, methodology): “Patient radiology reports and other medical record data are frequently used to generate noisy labels for imaging tasks [20,54–56].”
Deep Comprehensive Correlation Mining for Image Clustering (2019, background): “Instead of clustering, several approaches [3, 25, 34, 13, 39, 2, 47, 49] mainly focus on deep unsupervised learning of representations.”
Unsupervised Pre-Training of Image Features on Non-Curated Data (2019, unknown): “Various attempts have been made to train convnets using clustering [2, 3, 6, 29, 48, 52, 53].”