LFANet: Lightweight feature attention network for abnormal cell segmentation in cervical cytology images.

With the widely applied computer-aided diagnosis techniques in cervical cancer screening, cell segmentation has become a necessary step to determine the progression of cervical cancer. Traditional manual methods alleviate the dilemma caused by the shortage of medical resources to a certain extent. Unfortunately, with their low segmentation accuracy for abnormal cells, the complex process cannot realize an automatic diagnosis. In addition, various methods on deep learning can automatically extract image features with high accuracy and small error, making artificial intelligence increasingly popular in computer-aided diagnosis. However, they are not suitable for clinical practice because those complicated models would result in more redundant parameters from networks. To address the above problems, a lightweight feature attention network (LFANet), extracting differentially abundant feature information of objects with various resolutions, is proposed in this study. The model can accurately segment both the nucleus and cytoplasm regions in cervical images. Specifically, a lightweight feature extraction module is designed as an encoder to extract abundant features of input images, combining with depth-wise separable convolution, residual connection and attention mechanism. Besides, the feature layer attention module is added to precisely recover pixel location, which employs the global high-level information as a guide for the low-level features, capturing dependencies of channel features. Finally, our LFANet model is evaluated on all four independent datasets. The experimental results demonstrate that compared with other advanced methods, our proposed network achieves state-of-the-art performance with a low computational complexity.

Automatic Segmentation of Cervical Cells Based on Star-Convex Polygons in Pap Smear Images.

Cervical cancer is one of the most common cancers that threaten women's lives, and its early screening is of great significance for the prevention and treatment of cervical diseases. Pathologically, the accurate segmentation of cervical cells plays a crucial role in the diagnosis of cervical cancer. However, the frequent presence of adherent or overlapping cervical cells in Pap smear images makes separating them individually a difficult task. Currently, there are few studies on the segmentation of adherent cervical cells, and the existing methods commonly suffer from low segmentation accuracy and complex design processes. To address the above problems, we propose a novel star-convex polygon-based convolutional neural network with an encoder-decoder structure, called SPCNet. The model accomplishes the segmentation of adherent cells relying on three steps: automatic feature extraction, star-convex polygon detection, and non-maximal suppression (NMS). Concretely, a new residual-based attentional embedding (RAE) block is suggested for image feature extraction. It fuses the deep features from the attention-based convolutional layers with the shallow features from the original image through the residual connection, enhancing the network's ability to extract the abundant image features. And then, a polygon-based adaptive NMS (PA-NMS) algorithm is adopted to screen the generated polygon proposals and further achieve the accurate detection of adherent cells, thus allowing the network to completely segment the cell instances in Pap smear images. Finally, the effectiveness of our method is evaluated on three independent datasets. Extensive experimental results demonstrate that the method obtains superior segmentation performance compared to other well-established algorithms.

A novel medical image protection scheme using a 3-dimensional chaotic system.

Recently, great concerns have been raised regarding the issue of medical image protection due to the increasing demand for telemedicine services, especially the teleradiology service. To meet this challenge, a novel chaos-based approach is suggested in this paper. To address the security and efficiency problems encountered by many existing permutation-diffusion type image ciphers, the new scheme utilizes a single 3D chaotic system, Chen's chaotic system, for both permutation and diffusion. In the permutation stage, we introduce a novel shuffling mechanism, which shuffles each pixel in the plain image by swapping it with another pixel chosen by two of the three state variables of Chen's chaotic system. The remaining variable is used for quantification of pseudorandom keystream for diffusion. Moreover, the selection of state variables is controlled by plain pixel, which enhances the security against known/chosen-plaintext attack. Thorough experimental tests are carried out and the results indicate that the proposed scheme provides an effective and efficient way for real-time secure medical image transmission over public networks.

An efficient and secure medical image protection scheme based on chaotic maps.

Recently, the increasing demand for telemedicine services has raised interest in the use of medical image protection technology. Conventional block ciphers are poorly suited to image protection due to the size of image data and increasing demand for real-time teleradiology and other online telehealth applications. To meet this challenge, this paper presents a novel chaos-based medical image encryption scheme. To address the efficiency problem encountered by many existing permutation-substitution type image ciphers, the proposed scheme introduces a substitution mechanism in the permutation process through a bit-level shuffling algorithm. As the pixel value mixing effect is contributed by both the improved permutation process and the original substitution process, the same level of security can be achieved in a fewer number of overall rounds. The results indicate that the proposed approach provides an efficient method for real-time secure medical image transmission over public networks.