DXVNet-ViT-Huge (JFT) Multimode Classification Network Based on Vision Transformer

Aiming at the problem that traditional CNN network is not good at extracting global features of images, Based on DXVNet network, Conditional Random Fields (CRF) component and pre-trained ViT-Huge (Vision Transformer) are adopted in this paper Transformer model expands and builds a brand new DXVNet-ViT-Huge (JFT) network. CRF component can help the network learn the constraint conditions of each word corresponding prediction label, improve the D-GRU method based word label prediction errors, and improve the accuracy of sequence annotation. The Transformer architecture of the ViT (Huge) model can extract the global feature information of the image, while CNN is better at extracting the local features of the image. Therefore, the ViT (Huge) Huge pre-training model and CNN pre-training model adopt the multi-modal feature fusion technology. Two complementary image feature information is fused by Bi-GRU to improve the performance of network classification. The experimental results show that the newly constructed Dxvnet-Vit-Huge (JFT) model achieves good performance, and the F1 values in the two real public data sets are 6.03% and 7.11% higher than the original DXVNet model, respectively

Experimental and simulation study on nonlinear pitch control of Seagull underwater glider

1008-1015The Seagull underwater glider, developed by the Shanghai Jiao Tong University, is designed as a test-bed glider for the development and validation of various algorithms to enhance the glider’s long-term autonomy. In this paper, an adaptive backstepping control (ABC) method is proposed for the nonlinear pitch control of the underwater glider gliding in the vertical plane. The linear quadratic regulator (LQR) control and proportional-integral-derivative (PID) control are applied and evaluated with the ABC method to control a glider in saw-tooth motion. Simulation results demonstrate inherent effectiveness and superiority of the LQR or PID based method. According to Lyapunov stability theory, the ABC control scheme is derived to ensure the tracking errors asymptotically converge to zero. The ABC controller has been implemented on Seagull underwater glider, and verified in field experiments in the Qiandao Lake, Zhejiang

False Negative/Positive Control for SAM on Noisy Medical Images

The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation. It can use sparse manual prompts such as bounding boxes to generate pixel-level segmentation in natural images but struggles in medical images such as low-contrast, noisy ultrasound images. We propose a refined test-phase prompt augmentation technique designed to improve SAM's performance in medical image segmentation. The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy. We evaluate the method on two ultrasound datasets and show improvement in SAM's performance and robustness to inaccurate prompts, without the necessity for further training or tuning. Moreover, we present the Single-Slice-to-Volume (SS2V) method, enabling 3D pixel-level segmentation using only the bounding box annotation from a single 2D slice. Our results allow efficient use of SAM in even noisy, low-contrast medical images. The source code will be released soon

Distribution and Clinical Significance of Th17 Cells in the Tumor Microenvironment and Peripheral Blood of Pancreatic Cancer Patients

This study was designed to investigate the distribution of Th17 cells in the tumor microenvironment and peripheral blood of pancreatic cancer patients, its clinical significance, and the expression profile of Th17 cell-associated cytokines. The percentage of Th17 cells detected by flow cytometry analysis (FACS) was significantly higher in 46 pancreatic tumor tissues (5.28 ± 1.65%) compared with corresponding adjacent normal tissues (2.57 ± 0.83%) (P = 0.031). In addition, the percentage of Th17 cells was significantly higher in stage III-IV tumors than stage I-II tumors (P = 0.039). The percentage of Th17 cells in peripheral blood of 20 pancreatic cancer patients (3.99 ± 1.15%) was significantly higher than 15 healthy volunteers (1.98 ± 0.57%) (P = 0.027). Immunohistochemistry (IHC) was performed to detect IL-17+ cells in 46 pancreatic tumor tissues, as well as expression of CD34 in 24 tumor tissues. IL-17 was shown to mainly locate in cytoplasm, and the frequency of IL-17+ cells in tumor tissues (39/46) was higher than control (29/46). The presence of IL-17+ cells in tumor tissues was associated with tumor, node, and metastasis (TNM) stage, and lymph node metastasis (P = 0.012 and P = 0.009) but not with patient sex, age, tumor size, and histological grade (P > 0.05). Interestingly, distribution of Th17 cells in tumor tissues was positively correlated with microvessel density (MVD) (r = 0.86, P = 0.018). Furthermore, the median survival time of patients with high and low level of IL-17+ cells frequency was 14.5 and 18.5 months respectively (P = 0.023). The serum levels of Th17 cell-associated cytokines, IL-17 and IL-23 in 20 pancreatic patients detected by enzyme-linked immunosorbent assay (ELISA) were 69.2 ± 28.5 pg/mL and 266.5 ± 98.1 pg/mL, respectively, which were significantly higher than 15 healthy volunteers (P = 0.015 and P = 0.02). Moreover, levels of IL-17 and IL-23 were significantly higher in stage III-IV tumors than stage I-II tumors (P = 0.04 and P = 0.036). This study suggests that increase in Th17 cells frequency and its related cytokines levels in pancreatic tumor tissues may indicate involvement in the invasion and metastasis of pancreatic cancer, which may thereby affect patient prognosis. Therefore, Th17 cells and related cytokines may be served as important immune indicators for predicting the prognosis of pancreatic cancer patients