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

Trust-Worthy Semantic Communications for the Metaverse Relying on Federated Learning

As an evolving successor to the mobile Internet, the Metaverse creates the impression of an immersive environment, integrating the virtual as well as the real world. In contrast to the traditional mobile Internet based on servers, the Metaverse is constructed by billions of cooperating users by harnessing their smart edge devices having limited communication and computation resources. In this immersive environment an unprecedented amount of multi-modal data has to be processed. To circumvent this impending bottleneck, low-rate semantic communication might be harnessed in support of the Metaverse. But given that private multi-modal data is exchanged in the Metaverse, we have to guard against security breaches and privacy invasions. Hence we conceive a trust-worthy semantic communication system for the Metaverse based on a federated learning architecture by exploiting its distributed decision-making and privacy-preserving capability. We conclude by identifying a suite of promising research directions and open issues

Timing Recovery for Point-to-Multi-Point Coherent Passive Optical Networks

We propose a timing recovery for point-to-multi-point coherent passive optical networks. The results show that the proposed algorithm has low complexity and better robustness against the residual chromatic dispersion.Comment: The artical have been submitted to SPPCom conferenc

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

Comparison of serum apolipoprotein A-I between Chinese multiple sclerosis and other related autoimmune disease

<p>Abstract</p> <p>Background</p> <p>Serum apolipoprotein (apo) A-I was considered to be an immune regulator and could suppress pro-inflammatory cytokines generated by activated T cell in some autoimmune diseases. However, the change of serum apoA-I levels in multiple sclerosis (MS) patients is unknown.</p> <p>Methods</p> <p>In the presentation we performed a study on serum apoA-I levels in the patients with MS. We enrolled some age and gender matched patients with MS, autoimmune demyelinating diseases (Guillain-Barre Syndrome and Clinically Isolated Syndrome), neuroinflammatory diseases (viral encephalitis), autoimmune connective diseases (rheumatoid arthritis and systemic lupus erythematosus) and healthy control groups, and tested their serum lipids levels: total cholesterol (TC), triglyceride (TG), high-density lipoproteins (HDL), apolipoproteinB100 (apoB100), apolipoproteinA-I (apoA-I).</p> <p>Results</p> <p>For all patients, age had no effect on serum apoA-I levels (<it>P </it>> 0.05). Meanwhile, we proved the highest serum apoA-I levels in MS patients and the lowest serum apoA-I levels in SLE patients. Serum apoA-I levels was significantly elevated in female MS patients (P = 0.033; P < 0.05).</p> <p>Conclusion</p> <p>In short we believed that patients with MS and other autoimmune demyelination had significantly decreased serum levels of apo A-I.</p

Multilayered Molybdate Microflowers Fabricated by One-Pot Reaction for Efficient Water Splitting

The development of high-performance, low-cost and rapid-production bifunctional electrocatalysts towards overall water splitting still poses huge challenges. Herein, the authors utilize a facile hydrothermal method to synthesize a novel structure of Co-doped ammonium lanthanum molybdate on Ni foams (Co-ALMO@NF) as self-supported electrocatalysts. Owing to large active surfaces, lattice defect and conductive channel for rapid charge transport, Co-ALMO@NF exhibits good electrocatalytic performances which requires only 349/341 mV to achieve a high current density of 600 mA cm-2 for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), respectively. Besides, a low cell voltage of 1.52 V is required to reach the current density of 10 mA cm-2 in alkaline medium along with an excellent long-term stability for two-electrode configurations. Density functional theory calculations are performed to reveal the reaction mechanism on Co-ALMO@NF, which shows that the Mo site is the most favorable ones for HER, while the introduction of Co is beneficial to reduce the adsorption intensity on the surface of Co-ALMO@NF, thus accelerating OER process. This work highlighted the importance of the structural design for self-supporting electrocatalysts