Neural-like P systems are membrane computing models inspired by natural computing. Spiking neurral (SN) P systems, a kind of neural-like P systems, are viewed as third-generation neural network models. Although real neurons have complex structures, classical SN P systems simplify the structures and corresponding mechanisms to stationary two-dimensional graphs and lack related evolution mechanisms on spikes and channels, which limits the real applications of these models. In this paper, we propose a new hybrid SN P system with evolutionary channels (HN P systems), including three new types of rules for dynamically generating or removing one-one and one-many/many-one channels with related evolutions of spikes on the hybrid neuron structures. Two dynamic regulatory factors are also presented on rules to help guide the optimization of the HN P systems automatically. Based on the new P system, a multiple brain metastases (BMs) segmentation model is developed. The experimental results indicate that the proposed models outperform the state-of-the-art methods on the BMs, which have large variations in sizes, positions and shapes, and low contrast with their surroundings. Performances on the head and neck segmentation dataset also verifies the effectiveness of the HN P system.

Surgical smoke caused poor visibility during laparoscopic surgery, the smoke removal is important to improve the safety and efficiency of the surgery. We propose the Multilevel-feature-learning Attention-aware based Generative Adversarial Network for Removing Surgical Smoke (MARS-GAN) in this work. MARS-GAN incorporates multilevel smoke feature learning, smoke attention learning, and multi-task learning together. Specifically, the multilevel smoke feature learning adopts the multilevel strategy to adaptively learn non-homogeneity smoke intensity and area features with specific branches and integrates comprehensive features to preserve both semantic and textural information with pyramidal connections. The smoke attention learning extends the smoke segmentation module with the dark channel prior module to provide the pixel-wise measurement for focusing on the smoke features while preserving the smokeless details. And the multi-task learning strategy fuses the adversarial loss, cyclic consistency loss, smoke perception loss, dark channel prior loss, and contrast enhancement loss to help the model optimization. Furthermore, a paired smokeless/smoky dataset is synthesized for elevating smoke recognition ability. The experimental results show that MARS-GAN outperforms the comparative methods for removing surgical smoke on both synthetic/real laparoscopic surgical images, with the potential to be embedded in laparoscopic devices for smoke removal.

Neural-like P systems are membrane computing models inspired by natural computing and are viewed as third-generation neural network models. Although real neurons have complex structures, classical neural-like P systems simplify the structures and corresponding mechanisms to two-dimensional graphs or tree-based firing and forgetting communications, which limit the real applications of these models. In this paper, we propose a hypergraph-based numerical neural-like (HNN) P system containing five types of neurons to describe the high-order correlations among neuron structures. Three new kinds of communication mechanisms among neurons are also proposed to address numerical variables and functions. Based on the new neural-like P system, a tumor/organ segmentation model for medical images is developed. The experimental results indicate that the proposed models outperform the state-of-the-art methods based on two hippocampal datasets and a multiple brain metastases dataset, thus verifying the effectiveness of the HNN P system in correctly segmenting tumors/organs.

The emergence of infectious disease COVID-19 has challenged and changed the world in an unprecedented manner. The integration of wireless networks with edge computing (namely wireless edge networks) brings opportunities to address this crisis. In this paper, we aim to investigate the prediction of the infectious probability and propose precautionary measures against COVID-19 with the assistance of wireless edge networks. Due to the availability of the recorded detention time and the density of individuals within a wireless edge network, we propose a stochastic geometry-based method to analyze the infectious probability of individuals. The proposed method can well keep the privacy of individuals in the system since it does not require to know the location or trajectory of each individual. Moreover, we also consider three types of mobility models and the static model of individuals. Numerical results show that analytical results well match with simulation results, thereby validating the accuracy of the proposed model. Moreover, numerical results also offer many insightful implications. Thereafter, we also offer a number of countermeasures against the spread of COVID-19 based on wireless edge networks. This study lays the foundation toward predicting the infectious risk in realistic environment and points out directions in mitigating the spread of infectious diseases with the aid of wireless edge networks.

Accurate detection of human peripheral blood cells is of great significance to assist doctors to diagnose blood-related diseases. Traditional clinical detection of peripheral blood cells is usually identified by manual microscopy. However, such artificial analysis and processing methods are easily affected by subjective factors, which will cause certain errors. In recent years, the convolutional neural network has been applied to various medical image processing tasks and has shown satisfactory performance. However, traditional CNNs are limited by the lack of feature expression ability. This work introduces the idea of visual attention mechanism into the deep learning detection model and design an improved Squeeze-and-Excitation based YOLO-v3 detection model (ISE-YOLO). This model adds our improved SE module into the different structural blocks of the YOLO to strengthen the network information discrimination of input features and improve the detection performance. Experimental results demonstrate that the proposed ISE-YOLO improves the performance over 96.5% on WBCs, 92.7% on RBCs, and 89.6% on platelets, and outperforms other advanced classification methods.

Abstract This paper proposes an automated registration method for multi‐modality retinal fundus photographs based on the directional vessel skeleton. The main purpose is to register two retinal fundus photographs with different modalities of the same scanning region, which can provide multi‐modality information for clinicians to diagnose retinal diseases or to make a treatment decision. The directional vessel skeleton of each fundus image is first detected by bias field correction and Gabor filter. The final registered fundus photographs are then obtained by the iterative affine registration between the detected directional vessel skeletons of two photographs. In this work, four kinds of fundus photographs in the macular regions of the patient with diseases, consisting of 20 optical coherence tomography fundus images, 20 colour fundus photographs, 20 fluorescein fundus angiography images and 20 indocyanine green angiography images, are utilised to quantitatively evaluate the proposed method. The root‐mean‐square errors show an advantageous performance in both registration success rate and accuracy.

In this study, titanium disulfide (TiS 2 ) polyvinyl alcohol (PVA) film-type saturable absorber (SA) is synthesized with a modulation depth of 5.08% and a saturable intensity of 10.62 MW/cm 2 by liquid-phase exfoliation and spin-coating methods. Since TiS 2 -based SA has a strong nonlinear saturable absorption property, two types of optical soliton were observed in a mode-locked Er-doped fiber laser. When the pump power was raised to 67.3 mW, a conventional mode-locked pulse train with a repetition rate of 1.716 MHz and a pulse width of 6.57 ps was generated, and the output spectrum centered at 1556.98 nm and 0.466 nm spectral width with obvious Kelly sidebands was obtained. Another type of mode-locked pulse train with the maximum output power of 3.92 mW and pulse energy of 2.28 nJ at the pump power of 517.2 mW was achieved when the polarization controllers were adjusted. Since TiS 2 -based SA has excellent nonlinear saturable absorption characteristics, broad applications in ultrafast photonic are expected.

Automatic pancreas segmentation is crucial to the diagnostic assessment of diabetes or pancreatic cancer. However, the relatively small size of the pancreas in the upper body, as well as large variations of its location and shape in retroperitoneum, make the segmentation task challenging. To alleviate these challenges, in this article, we propose a cascaded multitask 3-D fully convolution network (FCN) to automatically segment the pancreas. Our cascaded network is composed of two parts. The first part focuses on fast locating the region of the pancreas, and the second part uses a multitask FCN with dense connections to refine the segmentation map for fine voxel-wise segmentation. In particular, our multitask FCN with dense connections is implemented to simultaneously complete tasks of the voxel-wise segmentation and skeleton extraction from the pancreas. These two tasks are complementary, that is, the extracted skeleton provides rich information about the shape and size of the pancreas in retroperitoneum, which can boost the segmentation of pancreas. The multitask FCN is also designed to share the low- and mid-level features across the tasks. A feature consistency module is further introduced to enhance the connection and fusion of different levels of feature maps. Evaluations on two pancreas datasets demonstrate the robustness of our proposed method in correctly segmenting the pancreas in various settings. Our experimental results outperform both baseline and state-of-the-art methods. Moreover, the ablation study shows that our proposed parts/modules are critical for effective multitask learning.

The classification of six types of white blood cells (WBCs) is considered essential for leukemia diagnosis, while the classification is labor-intensive and strict with the clinical experience. To relieve the complicated process with an efficient and automatic method, we propose the Attention-aware Residual Network based Manifold Learning model (ARML) to classify WBCs. The proposed ARML model leverages the adaptive attention-aware residual learning to exploit the category-relevant image-level features and strengthen the first-order feature representation ability. To learn more discriminatory information than the first-order ones, the second-order features are characterized. Afterwards, ARML encodes both the first- and second-order features with Gaussian embedding into the Riemannian manifold to learn the underlying non-linear structure of the features for classification. ARML can be trained in an end-to-end fashion, and the learnable parameters are iteratively optimized. 10800 WBCs images (1800 images for each type) is collected, 9000 images and five-fold cross-validation are used for training and validation of the model, while additional 1800 images for testing. The results show that ARML achieving average classification accuracy of 0.953 outperforms other state-of-the-art methods with fewer trainable parameters. In the ablation study, ARML achieves improved accuracy against its three variants: without manifold learning (AR), without attention-aware learning (RML), and AR without attention-aware learning. The t-SNE results illustrate that ARML has learned more distinguishable features than the comparison methods, which benefits the WBCs classification. ARML provides a clinically feasible WBCs classification solution for leukemia diagnose with an efficient manner.

Comprehensively analyzing the carotid artery is critically significant to diagnosing and treating cardiovascular diseases. The object of this work is to simultaneously achieve direct quantitative measurement and automated segmentation of the lumen diameter and intima-media thickness as well as the motion estimation of the carotid wall. No work has simultaneously achieved the comprehensive analysis of carotid artery due to three intractable challenges: 1) Tiny intima-media is more challenging to measure and segment; 2) Artifact generated by radial motion restrict the accuracy of measurement and segmentation; 3) Occlusions on diseased carotid walls generate dynamic complexity and indeterminacy. In this paper, we propose a novel optical flow-auxiliary multi-task regression network named OF-MSRN to overcome these challenges. We concatenate multi-scale features to a regression network to simultaneously achieve measurement and segmentation, which makes full use of the potential correlation between the two tasks. More importantly, we creatively explore an optical flow auxiliary module to take advantage of the co-promotion of segmentation and motion estimation to overcome the restrictions of the radial motion. Besides, we evaluate consistency between forward and backward optical flow to improve the accuracy of motion estimation of the diseased carotid wall. Extensive experiments on US sequences of 101 patients demonstrate the superior performance of OF-MSRN on the comprehensive analysis of the carotid artery by utilizing the dual optimization of the optical flow auxiliary module.

In this paper, we propose a novel automatic computer-aided method to detect polyps for colonoscopy videos. To capture perceptually and semantically meaningful salient polyp regions, we first segment images into multilevel superpixels. Each level corresponds to different sizes of superpixels. Rather than adopting hand-designed features to describe these superpixels in images, we employ sparse autoencoder (SAE) to learn discriminative features in an unsupervised way. Then, a novel unified bottom-up and top-down saliency method is proposed to detect polyps. In the first stage, we propose a weak bottom-up (WBU) saliency map by fusing the contrast-based saliency and object-center-based saliency together. The contrast-based saliency map highlights image parts that show different appearances compared with surrounding areas, whereas the object-center-based saliency map emphasizes the center of the salient object. In the second stage, a strong classifier with multiple kernel boosting is learned to calculate the strong top-down (STD) saliency map based on samples directly from the obtained multilevel WBU saliency maps. We finally integrate these two-stage saliency maps from all levels together to highlight polyps. Experiment results achieve 0.818 recall for saliency calculation, validating the effectiveness of our method. Extensive experiments on public polyp datasets demonstrate that the proposed saliency algorithm performs better compared with state-of-the-art saliency methods to detect polyps.

Audio is an important part of multimedia, and it has many useful applications in real life. Audio event classification is a key technology in audio management and application. Supervised audio event classification requires labeling large amounts of samples, while manual labeling is a very time-consuming work. In this paper we propose SVMCKNND, an active learning method for SVM classifier, to deal with the labeling problem in audio event classification. For SVMCKNND, in each iteration, first, a low-confidence region is delimited; then based on KNN, the samples that are more likely to be on the true class boundary are taken as the informative ones; finally, redundancy that exists in the informative samples is reduced to further decrease manual labeling workload. Experimental results show that SVMCKNNDperforms better than another two SVM active learning algorithms, especially in classifying small-sample audio events.

Registration of planning images with daily images is an important component for adaptive radiation therapy (ART). In this paper, a multiscale deformable registration framework is proposed by combining edge preserving scale space with the free form deformation (FFD) for registration of planning computed tomography (CT) images with daily cone beam CT (CBCT) images. The edge preserving scale space which is able to select edges and contours of an image according to their geometric size is derived from the total variation model with the L1 norm (TV-L1). At each scale, the selected edges and contours are sufficiently strong to drive the deformation using the FFD grid, then the deformation fields are gained by a coarse to fine manner. Furthermore, for automated registration we design an optimal estimation of the TV-L1 parameter by minimizing the defined offset. The experiments on CT and CBCT images show accuracy and robustness when compared to traditional methods.