A Hyper-Chaotically Encrypted Robust Digital Image Watermarking Method with Large Capacity Using Compress Sensing on a Hybrid Domain.

The digital watermarking technique is a quite promising technique for both image copyright protection and secure transmission. However, many existing techniques are not as one might have expected for robustness and capacity simultaneously. In this paper, we propose a robust semi-blind image watermarking scheme with a high capacity. Firstly, we perform a discrete wavelet transformation (DWT) transformation on the carrier image. Then, the watermark images are compressed via a compressive sampling technique for saving storage space. Thirdly, a Combination of One and Two-Dimensional Chaotic Map based on the Tent and Logistic map (TL-COTDCM) is used to scramble the compressed watermark image with high security and dramatically reduce the false positive problem (FPP). Finally, a singular value decomposition (SVD) component is used to embed into the decomposed carrier image to finish the embedding process. With this scheme, eight 256×256 grayscale watermark images are perfectly embedded into a 512×512 carrier image, the capacity of which is eight times over that of the existing watermark techniques on average. The scheme has been tested through several common attacks on high strength, and the experiment results show the superiority of our method via the two most used evaluation indicators, normalized correlation coefficient (NCC) values and the peak signal-to-noise ratio (PSNR). Our method outperforms the state-of-the-art in the aspects of robustness, security, and capacity of digital watermarking, which exhibits great potential in multimedia application in the immediate future.

ATFE-Net: Axial Transformer and Feature Enhancement-based CNN for ultrasound breast mass segmentation.

Breast mass is one of the main clinical symptoms of breast cancer. Recently, many CNN-based methods for breast mass segmentation have been proposed. However, these methods have difficulties in capturing long-range dependencies, causing poor segmentation of large-scale breast masses. In this paper, we propose an axial Transformer and feature enhancement-based CNN (ATFE-Net) for ultrasound breast mass segmentation. Specially, an axial Transformer (Axial-Trans) module and a Transformer-based feature enhancement (Trans-FE) module are proposed to capture long-range dependencies. Axial-Trans module only calculates self-attention in width and height directions of input feature maps, which reduces the complexity of self-attention significantly from O(n2) to O(n). In addition, Trans-FE module can enhance feature representation by capturing dependencies between different feature layers, since deeper feature layers have richer semantic information and shallower feature layers have more detailed information. The experimental results show that our ATFE-Net achieved better performance than several state-of-the-art methods on two publicly available breast ultrasound datasets, with Dice coefficient of 82.46% for BUSI and 86.78% for UDIAT, respectively.

Learning multi-frequency features in convolutional network for mammography classification.

Breast cancer is a common life-threatening disease among women. Computer-aided methods can provide second opinion or decision support for early diagnosis in mammography images. However, the whole images classification is highly challenging due to small sizes of lesion and slow contrast between lesions and fibro-glandular tissue. In this paper, inspired by conventional machine learning methods, we present a Multi Frequency Attention Network (MFA-Net) to highlight the salient features. The network decomposes the features into low spatial frequency components and high spatial frequency components, and then recalibrates discriminating features based on two-dimensional Discrete Cosine Transform in two different frequency parts separately. Low spatial frequency features help determine if there is a tumor while high spatial frequency features help focus more on the margin of the tumor. Our studies empirically show that compared to traditional convolutional neural network (CNN), the proposed method mitigates the influence of the margin of pectoral muscle and breast in mammography, which brings significant improvement. For malignant and benign classification, by using transfer learning, the proposed MFA-Net achieves the AUC index 91.71% on the INbreast dataset.

DCANet: Dual contextual affinity network for mass segmentation in whole mammograms.

PURPOSE: Breast mass segmentation in mammograms remains a crucial yet challenging topic in computer-aided diagnosis systems. Existing algorithms mainly used mass-centered patches to achieve mass segmentation, which is time-consuming and unstable in clinical diagnosis. Therefore, we aim to directly perform fully automated mass segmentation in whole mammograms with deep learning solutions. METHODS: In this work, we propose a novel dual contextual affinity network (a.k.a., DCANet) for mass segmentation in whole mammograms. Based on the encoder-decoder structure, two lightweight yet effective contextual affinity modules including the global-guided affinity module (GAM) and the local-guided affinity module (LAM) are proposed. The former aggregates the features integrated by all positions and captures long-range contextual dependencies, aiming to enhance the feature representations of homogeneous regions. The latter emphasizes semantic information around each position and exploits contextual affinity based on the local field-of-view, aiming to improve the indistinction among heterogeneous regions. RESULTS: The proposed DCANet is greatly demonstrated on two public mammographic databases including the DDSM and the INbreast, achieving the Dice similarity coefficient (DSC) of 85.95% and 84.65%, respectively. Both segmentation performance and computational efficiency outperform the current state-of-the-art methods. CONCLUSION: According to extensive qualitative and quantitative analyses, we believe that the proposed fully automated approach has sufficient robustness to provide fast and accurate diagnoses for possible clinical breast mass segmentation.

FS-UNet: Mass segmentation in mammograms using an encoder-decoder architecture with feature strengthening.

Breast mass segmentation in mammograms is still a challenging and clinically valuable task. In this paper, we propose an effective and lightweight segmentation model based on convolutional neural networks to automatically segment breast masses in whole mammograms. Specifically, we first developed feature strengthening modules to enhance relevant information about masses and other tissues and improve the representation power of low-resolution feature layers with high-resolution feature maps. Second, we applied a parallel dilated convolution module to capture the features of different scales of masses and fully extract information about the edges and internal texture of the masses. Third, a mutual information loss function was employed to optimise the accuracy of the prediction results by maximising the mutual information between the prediction results and the ground truth. Finally, the proposed model was evaluated on both available INbreast and CBIS-DDSM datasets, and the experimental results indicated that our method achieved excellent segmentation performance in terms of dice coefficient, intersection over union, and sensitivity metrics.

An integrated approach to identify distribution of Oncomelania hupensis, the intermediate host of Schistosoma japonicum, in a mountainous region in China.

The aim of this study is to better understand ecological variability related to the distribution of Oncomelania hupensis, the snail intermediate host of Schistosoma japonicum, and predict the spatial distribution of O. hupensis at the local scale in order to develop a more effective control strategy for schistosomiasis in the hilly and mountainous regions of China. A two-pronged approach was applied in this study consisting of a landscape pattern analysis complemented with Bayesian spatial modelling. The parasitological data were collected by cross-sectional surveys carried out in 11 villages in 2006 and mapped based on global positioning system (GPS) coordinates. Environmental surrogates and landscape metrics were derived from remotely-sensed images and land-cover/land-use classification data. Bayesian non-spatial and spatial models were applied to investigate the variation of snail density in relation to environmental surrogates and landscape metrics at the local scale. A Bayesian spatial model, validated by the deviance information criterion (DIC), was found to be the best-fitting model. The mean shape index (MSI) and Shannon's evenness indexes (SEI) were significantly associated with snail density. These findings suggest that decreasing the heterogeneity of the landscape can reduce snail density. A prediction maps were generated by the Bayesian model together with environmental surrogates and landscape metrics. In conclusion, the risk areas of snail distribution at the local scale can be identified using an integrated approach with landscape pattern analysis supported by remote sensing and GIS technologies, as well as Bayesian modelling.