Concerning the the size and morphological diversity of thyroid tissue and the complexity of surrounding tissue in thyroid ultrasound images, an ultrasound thyroid segmentation network based on feature fusion and dynamic multi-scale dilated convolution was proposed. Firstly, the dilated convolutions with different dilation rates and dynamic filters were used to fuse the global semantic information of different receptive domains and the semantic information in the context details with different ranges, so as to improve the adaptability and accuracy of the network to multi-scale targets. Then, the hybrid upsampling method was used to enhance the spatial information of high-dimensional semantic features and the context information of low-dimensional spatial features during feature dimensionality reduction. Finally, the spatial attention mechanism was introduced to optimize the low-dimensional features of the image, and the method of fusing high- and low-dimensional features was applied to retain the useful features of high- and low-dimensional feature information with the elimination of the redundant information and improve the network's ability to distinguish the background and foreground of the image. Experimental results show that the proposed method has an accuracy rate of 0.963±0.026, a recall rate of 0.84±0.03 and a dice coefficient of 0.79±0.03 in the public dataset of thyroid ultrasound images. It can be seen that the proposed method can solve the problems of large difference of tissue morphology and complex surrounding tissues.

At present, the research of face frontalization mainly solves the face yaw problem, and pays less attention to the face frontalization of the side face affected by yaw and pitch at the same time in real scenes such as surveillance video. Aiming at this problem and the problem of incomplete identity information retained in front face image generated by multi-angle side faces, a Generative Adversarial Network (GAN) based on feature map symmetry and periocular feature preserving loss was proposed. Firstly, according to the prior of face symmetry, a symmetry module of the feature map was proposed. The face key point detector was used to detect the position of nasal tip point, and mirror symmetry was performed to the feature map extracted by the encoder according to the nasal tip, so as to alleviate the lack of facial information at the feature level. Finally, benefiting from the idea of periocular recognition, the periocular feature preserving loss was added in the existing identity preserving method of generated image to train the generator to generate realistic and identity-preserving front face image. Experimental results show that the facial details of the images generated by the proposed algorithm were well preserved, and the average Rank-1 recognition rate of faces with all angles under the pitch of CAS-PEAL-R1 dataset is 99.03%, which can effectively solve the frontalization problem of multi-angle side faces.

In order to achieve more accurate diagnosis of benign and malignant of thyroid nodule ultrasound images and avoid unnecessary puncture or biopsy surgery, a feature combining method of conventional ultrasound imaging and ultrasound elastography based on Convolutional Neural Network (CNN) was proposed to improve the accuracy of benign and malignant classification of thyroid nodules. Firstly, large-scale natural image datasets were used by the convolutional network model for pre-training, and the feature parameters were transferred to the ultrasound image domain by transfer learning to generate depth features and process small samples. Then, the depth feature maps of conventional ultrasound imaging and ultrasound elastography were combined to form a hybrid feature space. Finally, the classification task was completed in the hybrid feature space, and an end-to-end convolution network model was constructed. The experiments were carried out on 1156 images, the proposed method had the accuracy of 0.924, which was higher than that of other single data source methods. The experimental results show that, the edge and texture features of the image are shared by the shallow convolutions, the abstract features of the high-level convolutions are related to the specific classification tasks, and the transfer learning method can solve the problem of insufficient data samples. At the same time, the elastic ultrasound image can objectively quantify the lesion hardness of thyroid nodules, and with the combination of the texture contour features of conventional ultrasound image, the mixed features can more fully describe the differences between different lesions. Therefore, this method can effectively and accurately classify the thyroid nodules, reduce the pain of patients, and provide doctors with more accurate auxiliary diagnostic information.

Feature extraction and pattern classification are two key problems in face recognition. In order to solve the high-dimensional and Small Sample Size (SSS) problem of face recognition, start with the feature extraction of human face and dimensionality reduction algorithms, a quadratic feature extraction and dimensionality reduction algorithm model was put forward based on Restricted Boltzmann Machine (RBM). At first, the image was evenly divided into a number of local image blocks and quantified, then the image was processed by Gabor wavelet transformation. The Gabor facial features were encoded by RBM to learn more intrinsic characteristics of data, so as to achieve the purpose of dimensionality reduction of high-dimensional facial features. On the basis of that, a multimodal face recognition algorithm based on Deep Belief Network (DBN) was proposed. The recognition results on ORL, UMIST and FERET face databases with different sample sizes and different resolution images show that, compared with the linear dimension reduction method and shallow network method, the proposed method achieves better learning efficiency and good recognition result.