An Unsupervised Deep-Learning Method for Bone Age Assessment

The bone age, reflecting the degree of development of the bones, can be used to predict the adult height and detect endocrine diseases of children. Both examinations of radiologists and variability of operators have a significant impact on bone age assessment. To decrease human intervention , machine learning algorithms are used to assess the bone age automatically. However, conventional supervised deep-learning methods need pre-labeled data. In this paper, based on the convolutional auto-encoder with constraints (CCAE), an unsupervised deep-learning model proposed in the classification of the fingerprint, we propose this model for the classification of the bone age and baptize it BA-CCAE. In the proposed BA-CCAE model, the key regions of the raw X-ray images of the bone age are encoded, yielding the latent vectors. The K-means clustering algorithm is used to obtain the final classifications by grouping the latent vectors of the bone images. A set of experiments on the Radiological Society of North America pediatric bone age dataset (RSNA) show that the accuracy of classifications at 48-month intervals is 76.15%. Although the accuracy now is lower than most of the existing supervised models, the proposed BA-CCAE model can establish the classification of bone age without any pre-labeled data, and to the best of our knowledge, the proposed BA-CCAE is one of the few trails using the unsupervised deep-learning method for the bone age assessment.

Early Abnormal Detection of Sewage Pipe Network: Bagging of Various Abnormal Detection Algorithms

Abnormalities of the sewage pipe network will affect the normal operation of the whole city. Therefore, it is important to detect the abnormalities early. This paper propose an early abnormal-detection method. The abnormalities are detected by using the conventional algorithms, such as isolation forest algorithm, two innovations are given: (1) The current and historical data measured by the sensors placed in the sewage pipe network (such as ultrasonic Doppler flowmeter) are taken as the overall dataset, and then the general dataset is detected by using the conventional anomaly detection method to diagnose the anomaly of the data. The anomaly refers to the sample different from the others samples in the whole dataset. Because the definition of anomaly is not through the algorithm, but the whole dataset, the construction of the whole dataset is the key to propose the early abnormal-detection algorithms. (2) A bagging strategy for a variety of conventional anomaly detection algorithms is proposed to achieve the early detection of anomalies with the high precision and recall. The results show that this method can achieve the early anomaly detection with the highest precision of 98.21%, the recall rate 63.58% and F1-score of 0.774.

An Unsupervised Deep-Learning Method for Fingerprint Classification: the CCAE Network and the Hybrid Clustering Strategy

The fingerprint classification is an important and effective method to quicken the process and improve the accuracy in the fingerprint matching process. Conventional supervised methods need a large amount of pre-labeled data and thus consume immense human resources. In this paper, we propose a new and efficient unsupervised deep learning method that can extract fingerprint features and classify fingerprint patterns automatically. In this approach, a new model named constraint convolutional auto-encoder (CCAE) is used to extract fingerprint features and a hybrid clustering strategy is applied to obtain the final clusters. A set of experiments in the NIST-DB4 dataset shows that the proposed unsupervised method exhibits the efficient performance on fingerprint classification. For example, the CCAE achieves an accuracy of 97.3% on only 1000 unlabeled fingerprints in the NIST-DB4.

An Effective and Efficient Method to Solve the High-Order and the Non-Linear Ordinary Differential Equations: the Ratio Net

An effective and efficient method that solves the high-order and the non-linear ordinary differential equations is provided. The method is based on the ratio net. By comparing the method with existing methods such as the polynomial based method and the multilayer perceptron network based method, we show that the ratio net gives good results and has higher efficiency.

Networks with pixels embedding: a method to improve noise resistance in images classification

In the task of images classification, usually, the network is sensitive to noises. For example, an image of cat with noises might be misclassified as an ostrich. Conventionally, to overcome the problem of noises, one uses the technique of data enhancement, that is, to teach the network to distinguish noises by adding more images with noises in the training dataset. In this work, we provide a noise-resistance network in images classification by introducing a technique of pixels embedding. We test the network with pixels embedding, which is abbreviated as the network with PE, on the mnist database of handwritten digits. It shows that the network with PE outperforms the conventional network on images with noises. The technique of pixels embedding can be used in many tasks of images classification to improve noise resistance.

Activation functions are not needed: the ratio net

The function approximator that finds the function mapping the feature to the label is an important component in a deep neural network for classification tasks. To overcome nonlinearity, which is the main difficulty in designing the function approximator, one usually uses the method based on the nonlinear activation function or the nonlinear kernel function and yields classical networks such as the feed-forward neural network (MLP) and the radial basis function network (RBF). Although, classical networks such as the MLP are robust in most of the classification task, they are not the most efficient. E.g., they use large amount of parameters and take long times to train. Additionally, the choice of activation functions has a non-negligible influence on the effectiveness and efficiency of the network. In this paper, we propose a new network that is efficient in finding the function that maps the feature to the label. Instead of using the nonlinear activation function, the new proposed network uses the fractional form to overcome the nonlinearity, thus for the sake of convenience, we name the network the ratio net. We compare the effectiveness and efficiency of the ratio net and the classical networks such as the MLP and the RBF in the classification task on the mnist database of handwritten digits and the IMDb dataset which is a binary sentiment analysis dataset. The result shows that the ratio net outperforms both the MLP and the RBF.