A Semisupervised Learning Framework for Recognition and Classification of Defects in Transient Thermography Detection

Abstract Objective This article presents a novel method of semisupervised learning using convolutional autoencoders for optical endomicroscopic images. Optical endomicroscopy (OE) is a newly emerged biomedical imaging modality that can support real-time clinical decisions for the grade of dysplasia. To enable real-time decision making, computer-aided diagnosis (CAD) is essential for its high speed and objectivity. However, traditional supervised CAD requires a large amount of training data. Compared with the limited number of labeled images, we can collect a larger number of unlabeled images. To utilize these unlabeled images, we have developed a Convolutional AutoEncoder based Semi-supervised Network (CAESNet) for improving the classification performance. Materials and Methods We applied our method to an OE dataset collected from patients undergoing endoscope-based confocal laser endomicroscopy procedures for Barrett’s esophagus at Emory Hospital, which consists of 429 labeled images and 2826 unlabeled images. Our CAESNet consists of an encoder with 5 convolutional layers, a decoder with 5 transposed convolutional layers, and a classification network with 2 fully connected layers and a softmax layer. In the unsupervised stage, we first update the encoder and decoder with both labeled and unlabeled images to learn an efficient feature representation. In the supervised stage, we further update the encoder and the classification network with only labeled images for multiclass classification of the OE images. Results Our proposed semisupervised method CAESNet achieves the best average performance for multiclass classification of OE images, which surpasses the performance of supervised methods including standard convolutional networks and convolutional autoencoder network. Conclusions Our semisupervised CAESNet can efficiently utilize the unlabeled OE images, which improves the diagnosis and decision making for patients with Barrett’s esophagus.

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On the Impact of Labeled Sample Selection in Semisupervised Learning for Complex Visual Recognition Tasks

Complexity ◽

10.1155/2018/6531203 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Eftychios Protopapadakis ◽

Athanasios Voulodimos ◽

Anastasios Doulamis

Keyword(s):

Visual Recognition ◽

Clustering Algorithm ◽

Sample Selection ◽

Semisupervised Learning ◽

Learning Approaches ◽

Training Set ◽

Learning Framework ◽

Workflow Monitoring ◽

Defect Recognition ◽

The Impact

One of the most important aspects in semisupervised learning is training set creation among a limited amount of labeled data in such a way as to maximize the representational capability and efficacy of the learning framework. In this paper, we scrutinize the effectiveness of different labeled sample selection approaches for training set creation, to be used in semisupervised learning approaches for complex visual pattern recognition problems. We propose and explore a variety of combinatory sampling approaches that are based on sparse representative instances selection (SMRS), OPTICS algorithm, k-means clustering algorithm, and random selection. These approaches are explored in the context of four semisupervised learning techniques, i.e., graph-based approaches (harmonic functions and anchor graph), low-density separation, and smoothness-based multiple regressors, and evaluated in two real-world challenging computer vision applications: image-based concrete defect recognition on tunnel surfaces and video-based activity recognition for industrial workflow monitoring.

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