DSFNet: Convolutional Encoder-Decoder Architecture Combined Dual-GCN and Stand-alone Self-attention by Fast Normalized Fusion for Polyps Segmentation

In the past few decades, deep learning technology has been widely used in medical image segmentation and has made significant breakthroughs in the fields of liver and liver tumor segmentation, brain and brain tumor segmentation, video disc segmentation, heart image segmentation, and so on. However, the segmentation of polyps is still a challenging task since the surface of the polyps is flat and the color is very similar to that of surrounding tissues. Thus, It leads to the problems of the unclear boundary between polyps and surrounding mucosa, local overexposure, and bright spot reflection. To counter this problem, this paper presents a novel U-shaped network, namely DSFNet, which effectively combines the advantages of Dual-GCN and self-attention mechanisms. First, we introduce a feature enhancement block module based on Dual-GCN module as an attention mechanism to enhance the feature extraction of local spatial and structural information with fine granularity. Second, the stand-alone self-attention module is designed to enhance the integration ability of the decoding stage model to global information. Finally, the Fast Normalized Fusion method with trainable weights is used to efficiently fuse the corresponding three feature graphs in encoding, bottleneck, and decoding blocks, thus promoting information transmission and reducing the semantic gap between encoder and decoder. Our model is tested on two public datasets including Endoscene and Kvasir-SEG and compared with other state-of-the-art models. Experimental results show that the proposed model surpasses other competitors in many indicators, such as Dice, MAE, and IoU. In the meantime, ablation studies are also conducted to verify the efficacy and effectiveness of each module. Qualitative and quantitative analysis indicates that the proposed model has great clinical significance.Comment: 10 pages, 6 figures, 3 table

Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model

Deep learning (DL) based contrast dose reduction and elimination in MRI imaging is gaining traction, given the detrimental effects of Gadolinium-based Contrast Agents (GBCAs). These DL algorithms are however limited by the availability of high quality low dose datasets. Additionally, different types of GBCAs and pathologies require different dose levels for the DL algorithms to work reliably. In this work, we formulate a novel transformer (Gformer) based iterative modelling approach for the synthesis of images with arbitrary contrast enhancement that corresponds to different dose levels. The proposed Gformer incorporates a sub-sampling based attention mechanism and a rotational shift module that captures the various contrast related features. Quantitative evaluation indicates that the proposed model performs better than other state-of-the-art methods. We further perform quantitative evaluation on downstream tasks such as dose reduction and tumor segmentation to demonstrate the clinical utility.Comment: Accepted in MICCAI 202

SQ-Swin: a Pretrained Siamese Quadratic Swin Transformer for Lettuce Browning Prediction

Packaged fresh-cut lettuce is widely consumed as a major component of vegetable salad owing to its high nutrition, freshness, and convenience. However, enzymatic browning discoloration on lettuce cut edges significantly reduces product quality and shelf life. While there are many research and breeding efforts underway to minimize browning, the progress is hindered by the lack of a rapid and reliable methodology to evaluate browning. Current methods to identify and quantify browning are either too subjective, labor intensive, or inaccurate. In this paper, we report a deep learning model for lettuce browning prediction. To the best of our knowledge, it is the first-of-its-kind on deep learning for lettuce browning prediction using a pretrained Siamese Quadratic Swin (SQ-Swin) transformer with several highlights. First, our model includes quadratic features in the transformer model which is more powerful to incorporate real-world representations than the linear transformer. Second, a multi-scale training strategy is proposed to augment the data and explore more of the inherent self-similarity of the lettuce images. Third, the proposed model uses a siamese architecture which learns the inter-relations among the limited training samples. Fourth, the model is pretrained on the ImageNet and then trained with the reptile meta-learning algorithm to learn higher-order gradients than a regular one. Experiment results on the fresh-cut lettuce datasets show that the proposed SQ-Swin outperforms the traditional methods and other deep learning-based backbones

Clinical benefits of oral anticoagulants in atrial fibrillation patients with dementia: a systematic review and meta-analysis

BackgroundThe management of atrial fibrillation (AF) with oral anticoagulants (OAC) is generally recommended to reduce the risk of stroke. However, the decision to prescribe these medications for patients with AF and dementia remains controversial.MethodsA systematic review and meta-analysis of retrospective cohort studies were conducted. The search encompassed PubMed, Cochrane Library, Web of Science, and Embase databases from inception until May 1st, 2023, with language limited to English. Eligible studies included comparisons between exposure to OAC vs. non-OAC in the AF population with dementia or cognitive impairment. Studies that compared the effects of direct oral anticoagulants (DOAC) and vitamin-K antagonists were also included. The primary outcome was all-cause mortality, and the secondary outcomes were ischemic stroke and major bleeding. This study was registered with PROSPERO (No. CRD42023420678).ResultsA total of five studies (N = 21,962 patients) met the eligibility criteria and were included in this review. The follow-up duration ranged from 1 to 4 years. Meta-analysis demonstrated that OAC treatment was associated with a lower risk of all-cause mortality in AF patients with dementia with a hazard ratio (HR) of 0.79 and a 95% confidence interval (CI) ranging from 0.68 to 0.92, compared to non-OAC treatment. No statistical differences were observed in the risk of major bleeding (HR = 1.12, 95% CI: 0.88–1.42) or ischemic stroke (HR = 0.77, 95% CI: 0.58–1.00). Three studies reported comparisons between DOAC and warfarin; however, pooled analysis was not performed due to heterogeneity.ConclusionThe use of OACs in individuals diagnosed with both AF and dementia holds the potential to reduce all-cause mortality rates, thereby improving the overall clinical prognosis within this specific population.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023420678, PROSPERO identifier, CRD42023420678

LoMAE: Low-level Vision Masked Autoencoders for Low-dose CT Denoising

Low-dose computed tomography (LDCT) offers reduced X-ray radiation exposure but at the cost of compromised image quality, characterized by increased noise and artifacts. Recently, transformer models emerged as a promising avenue to enhance LDCT image quality. However, the success of such models relies on a large amount of paired noisy and clean images, which are often scarce in clinical settings. In the fields of computer vision and natural language processing, masked autoencoders (MAE) have been recognized as an effective label-free self-pretraining method for transformers, due to their exceptional feature representation ability. However, the original pretraining and fine-tuning design fails to work in low-level vision tasks like denoising. In response to this challenge, we redesign the classical encoder-decoder learning model and facilitate a simple yet effective low-level vision MAE, referred to as LoMAE, tailored to address the LDCT denoising problem. Moreover, we introduce an MAE-GradCAM method to shed light on the latent learning mechanisms of the MAE/LoMAE. Additionally, we explore the LoMAE's robustness and generability across a variety of noise levels. Experiments results show that the proposed LoMAE can enhance the transformer's denoising performance and greatly relieve the dependence on the ground truth clean data. It also demonstrates remarkable robustness and generalizability over a spectrum of noise levels

An archetypal determination of mobile cloud computing for emergency applications using decision tree algorithm.

Numerous users are experiencing unsafe communications due to the growth of big network mediums, where no node communication is detected in emergency scenarios. Many people find it difficult to communicate in emergency situations as a result of such communications. In this paper, a mobile cloud computing procedure is implemented in the suggested technique in order to prevent such circumstances, and to make the data transmission process more effective. An analytical framework that addresses five significant minimization and maximization objective functions is used to develop the projected model. Additionally, all mobile cloud computing nodes are designed with strong security, ensuring that all the resources are allocated appropriately. In order to isolate all the active functions, the analytical framework is coupled with a machine learning method known as Decision Tree. The suggested approach benefits society because all cloud nodes can extend their assistance in times of need at an affordable operating and maintenance cost. The efficacy of the proposed approach is tested in five scenarios, and the results of each scenario show that it is significantly more effective than current case studies on an average of 86%

Ion‐Specific Oil Repellency of Polyelectrolyte Multilayers in Water: Molecular Insights into the Hydrophilicity of Charged Surfaces

Surface wetting on polyelectrolyte multilayers (PEMs), prepared by alternating deposition of polydiallyldimethylammonium chloride (PDDA) and poly(styrene sulfonate) (PSS), was investigated mainly in water‐solid‐oil systems. The surface‐wetting behavior of as‐prepared PEMs was well correlated to the molecular structures of the uncompensated ionic groups on the PEMs as revealed by sum frequency generation vibrational and X‐ray photoelectron spectroscopies. The orientation change of the benzenesulfonate groups on the PSS‐capped surfaces causes poor water wetting in oil or air and negligible oil wetting in water, while the orientation change of the quaternized pyrrolidine rings on the PDDA‐capped surfaces hardly affects their wetting behavior. The underwater oil repellency of PSS‐capped PEMs was successfully harnessed to manufacture highly efficient filters for oil‐water separation at high flux.Wet surfaces: Liquid wetting on charged surfaces is well correlated with the molecular nature of surface ionic groups. The orientation change of surface ionic groups either hardly affects water wetting if their configuration is isotropic, or markedly transforms poor water wetting in oil to poor water de‐wetting in water if their configuration is anisotropic, thus leading to excellent underwater oil repellency.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111271/1/anie_201411992_sm_miscellaneous_information.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/111271/2/4851_ftp.pd

Topological Microlaser with A non-Hermitian Topological Bulk

Bulk-edge correspondence, with quantized bulk topology leading to protected edge states, is a hallmark of topological states of matter and has been experimentally observed in electronic, atomic, photonic, and many other systems. While bulk-edge correspondence has been extensively studied in Hermitian systems, a non-Hermitian bulk could drastically modify the Hermitian topological band theory due to the interplay between non-Hermiticity and topology; and its effect on bulk-edge correspondence is still an ongoing pursuit. Importantly, including non-Hermicity can significantly expand the horizon of topological states of matter and lead to a plethora of unique properties and device applications, an example of which is a topological laser. However, the bulk topology, and thereby the bulk-edge correspondence, in existing topological edge-mode lasers is not well defined. Here, we propose and experimentally probe topological edge-mode lasing with a well-defined non-Hermitian bulk topology in a one-dimensional (1D) array of coupled ring resonators. By modeling the Hamiltonian with an additional degree of freedom (referred to as synthetic dimension), our 1D structure is equivalent to a 2D non-Hermitian Chern insulator with precise mapping. Our work may open a new pathway for probing non-Hermitian topological effects and exploring non-Hermitian topological device applications.Comment: 8 pages, 4 figure