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.

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.

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.