Thinking Twice: Clinical-Inspired Thyroid Ultrasound Lesion Detection Based on Feature Feedback

Accurate detection of thyroid lesions is a critical aspect of computer-aided diagnosis. However, most existing detection methods perform only one feature extraction process and then fuse multi-scale features, which can be affected by noise and blurred features in ultrasound images. In this study, we propose a novel detection network based on a feature feedback mechanism inspired by clinical diagnosis. The mechanism involves first roughly observing the overall picture and then focusing on the details of interest. It comprises two parts: a feedback feature selection module and a feature feedback pyramid. The feedback feature selection module efficiently selects the features extracted in the first phase in both space and channel dimensions to generate high semantic prior knowledge, which is similar to coarse observation. The feature feedback pyramid then uses this high semantic prior knowledge to enhance feature extraction in the second phase and adaptively fuses the two features, similar to fine observation. Additionally, since radiologists often focus on the shape and size of lesions for diagnosis, we propose an adaptive detection head strategy to aggregate multi-scale features. Our proposed method achieves an AP of 70.3% and AP50 of 99.0% on the thyroid ultrasound dataset and meets the real-time requirement. The code is available at https://github.com/HIT-wanglingtao/Thinking-Twice.Comment: 20 pages, 11 figures, released code for https://github.com/HIT-wanglingtao/Thinking-Twic

CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network

U-Net and its extensions have achieved great success in medical image segmentation. However, due to the inherent local characteristics of ordinary convolution operations, U-Net encoder cannot effectively extract global context information. In addition, simple skip connections cannot capture salient features. In this work, we propose a fully convolutional segmentation network (CMU-Net) which incorporates hybrid convolutions and multi-scale attention gate. The ConvMixer module extracts global context information by mixing features at distant spatial locations. Moreover, the multi-scale attention gate emphasizes valuable features and achieves efficient skip connections. We evaluate the proposed method using both breast ultrasound datasets and a thyroid ultrasound image dataset; and CMU-Net achieves average Intersection over Union (IoU) values of 73.27% and 84.75%, and F1 scores of 84.81% and 91.71%. The code is available at https://github.com/FengheTan9/CMU-Net.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion

The U-shaped architecture has emerged as a crucial paradigm in the design of medical image segmentation networks. However, due to the inherent local limitations of convolution, a fully convolutional segmentation network with U-shaped architecture struggles to effectively extract global context information, which is vital for the precise localization of lesions. While hybrid architectures combining CNNs and Transformers can address these issues, their application in real medical scenarios is limited due to the computational resource constraints imposed by the environment and edge devices. In addition, the convolutional inductive bias in lightweight networks adeptly fits the scarce medical data, which is lacking in the Transformer based network. In order to extract global context information while taking advantage of the inductive bias, we propose CMUNeXt, an efficient fully convolutional lightweight medical image segmentation network, which enables fast and accurate auxiliary diagnosis in real scene scenarios. CMUNeXt leverages large kernel and inverted bottleneck design to thoroughly mix distant spatial and location information, efficiently extracting global context information. We also introduce the Skip-Fusion block, designed to enable smooth skip-connections and ensure ample feature fusion. Experimental results on multiple medical image datasets demonstrate that CMUNeXt outperforms existing heavyweight and lightweight medical image segmentation networks in terms of segmentation performance, while offering a faster inference speed, lighter weights, and a reduced computational cost. The code is available at https://github.com/FengheTan9/CMUNeXt.Comment: 8 pages, 3 figure

A Method of Two-Stage Pressure Control Based on Multistage Orifices

The interaction of pressure and flow in a hydraulic system with multiple working conditions, multiple actuators, and large flow limits action adjustment and control. Through a pilot pressure control circuit, hydraulically operated valves can adjust pressure or direction more effectively. A recent study proposed a two-stage pressure control method based on multistage orifices and solenoid valves. The requirements of the number and diameter ratio of short orifices in the series to realize the two-stage pressure control were theoretically analyzed. Scheme design and experiment were carried out. The influence of structures of complex flow channel and solenoid valve on the higher or lower pilot control pressure was considered in the experiment. The method was experimentally verified and successfully applied in a turbine electrohydraulic control system with lower maintenance costs, making the system more reliable in the case of electrical failure. Research results provide insight into pilot pressure control in fluid systems using multistage orifices to achieve either higher or lower pressure. In addition, it has important guiding significance for the design of valves or engineering systems based on pilot hydraulic pressure

Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model

Medical image segmentation is a critical step in computer-aided diagnosis, and convolutional neural networks are popular segmentation networks nowadays. However, the inherent local operation characteristics make it difficult to focus on the global contextual information of lesions with different positions, shapes, and sizes. Semi-supervised learning can be used to learn from both labeled and unlabeled samples, alleviating the burden of manual labeling. However, obtaining a large number of unlabeled images in medical scenarios remains challenging. To address these issues, we propose a Multi-level Global Context Cross-consistency (MGCC) framework that uses images generated by a Latent Diffusion Model (LDM) as unlabeled images for semi-supervised learning. The framework involves of two stages. In the first stage, a LDM is used to generate synthetic medical images, which reduces the workload of data annotation and addresses privacy concerns associated with collecting medical data. In the second stage, varying levels of global context noise perturbation are added to the input of the auxiliary decoder, and output consistency is maintained between decoders to improve the representation ability. Experiments conducted on open-source breast ultrasound and private thyroid ultrasound datasets demonstrate the effectiveness of our framework in bridging the probability distribution and the semantic representation of the medical image. Our approach enables the effective transfer of probability distribution knowledge to the segmentation network, resulting in improved segmentation accuracy. The code is available at https://github.com/FengheTan9/Multi-Level-Global-Context-Cross-Consistency.Comment: 10 pages, 8 figures, Released code for https://github.com/FengheTan9/Multi-Level-Global-Context-Cross-Consistenc

Adsorption of Tetracycline with Reduced Graphene Oxide Decorated with MnFe2O4 Nanoparticles

Abstract Nanomaterials were widely used as efficient adsorbents for environmental remediation of tetracycline pollution. However, the separation of the adsorbents posed the challenge to their practical applications. In this study, we grew magnetic MnFe2O4 nanoparticles on the reduced graphene oxide (rGO) to form MnFe2O4/rGO nanocomposite with a one-step method. When used as the absorbent of Tetracycline, it exhibited an adsorption capacity of 41 mg/g. The adsorption kinetics and isotherm were fitted well with the pseudo-second order model and Freundlich model, respectively. The MnFe2O4/rGO nanocomposite could be easily extracted from the solution with the external magnetic field and regenerated with acid washing