Decoding Path Research Articles

Multi-Scale Hierarchical Feature Fusion for Infrared Small-Target Detection

Detecting small targets in infrared images presents significant challenges due to their tiny size and complex backgrounds, making this task a hotspot for research. Traditional methods rely on assumption-based modeling and manual design, struggling to handle the variability of real-world scenarios. Although convolutional neural networks (CNNs) increase robustness to diverse scenes with a data-driven paradigm, many CNN-based methods are insufficient in capturing fine-grained details necessary for small targets and are less effective during multi-scale feature fusion. To overcome these challenges, we propose the novel Wide-scale Gated Fully Fusion Network (WGFFNet) in this article, which contributes to infrared small-target detection (IRSTD). WGFFNet uses a classic encoder–decoder structure, where the designed stepped fusion block (SFB) embedded in the feature extraction stage captures finer local context across multiple scales during encoding, and along the decoding path, the multi-level features are progressively integrated by a Fully Gated Interaction (FGI) Module to enhance feature representation. The inclusion of a boundary difference loss further optimizes the edge details of targets. We conducted comprehensive experiments on two public infrared small-target datasets: SIRST-V2 and IRSTD-1k. Quantitative and qualitative results demonstrate that our WGFFNet outperforms representative methods when considering various evaluation metrics together, achieving an improved detection performance and computational efficiency for detecting small targets in infrared images.

A Modified Bi-Directional Convolutional U-Net (BCDU-Net) Neural Network Approach for Lung CT Image Segmentation

In this paper, a modified Bi-directional Convolutional Long Short-Term Memory U-Net (BCDU-Net) neural network is presented, which aims at enhancing medical image segmentation for lung cancer diagnosis. By integrating BConvLSTM in the decoding path and densely connected convolutional layers in the encoding path, the proposed model achieves greater stability and precision in segmenting lung CT images. The addition of Batch Normalization (BN) after the up-convolutional layers accelerates convergence speed by six times. A notable feature of BCDU-Net is its adaptability to different imaging modalities, enabling it to generalize across diverse data sources by reducing overfitting, a limitation seen in many existing models. This adaptability allows the model’s ability to be integrated into various clinical environments, ensuring consistent and reliable results across different equipment. Another key contribution is the enhanced interpretability of the model, a critical improvement when compared with traditional U-Net models. BCDU-Net accurately segments complex anatomical structures, such as the left and right lungs, and precisely identifies tumors near central bronchi or trachea airways, which is crucial for lung cancer treatment planning. The proposed model was tested on the Cancer Imaging Archive (TCIA) dataset from the 2017 Lung CT Segmentation Challenge, achieving Dice Similarity Coefficients (DSC) of 97.97 for the right lung and 97.73 for the left lung. Overall, the BCDU-Net model demonstrates superior accuracy and interpretability in medical image segmentation and holds promise for broader applications in medical imaging beyond lung cancer diagnosis.

LDDP-Net: A Lightweight Neural Network with Dual Decoding Paths for Defect Segmentation of LED Chips.

Chip defect detection is a crucial aspect of the semiconductor production industry, given its significant impact on chip performance. This paper proposes a lightweight neural network with dual decoding paths for LED chip segmentation, named LDDP-Net. Within the LDDP-Net framework, the receptive field of the MobileNetv3 backbone is modified to mitigate information loss. In addition, dual decoding paths consisting of a coarse decoding path and a fine-grained decoding path in parallel are developed. Specifically, the former employs a straightforward upsampling approach, emphasizing macro information. The latter is more detail-oriented, using multiple pooling and convolution techniques to focus on fine-grained information after deconvolution. Moreover, the integration of intermediate-layer features into the upsampling operation enhances boundary segmentation. Experimental results demonstrate that LDDP-Net achieves an mIoU (mean Intersection over Union) of 90.29% on the chip dataset, with parameter numbers and FLOPs (Floating Point Operations) of 2.98 M and 2.24 G, respectively. Comparative analyses with advanced methods reveal varying degrees of improvement, affirming the effectiveness of the proposed method.

Advancing semantic segmentation: Enhanced UNet algorithm with attention mechanism and deformable convolution.

This paper presents a novel method for improving semantic segmentation performance in computer vision tasks. Our approach utilizes an enhanced UNet architecture that leverages an improved ResNet50 backbone. We replace the last layer of ResNet50 with deformable convolution to enhance feature representation. Additionally, we incorporate an attention mechanism, specifically ECA-ASPP (Attention Spatial Pyramid Pooling), in the encoding path of UNet to capture multi-scale contextual information effectively. In the decoding path of UNet, we explore the use of attention mechanisms after concatenating low-level features with high-level features. Specifically, we investigate two types of attention mechanisms: ECA (Efficient Channel Attention) and LKA (Large Kernel Attention). Our experiments demonstrate that incorporating attention after concatenation improves segmentation accuracy. Furthermore, we compare the performance of ECA and LKA modules in the decoder path. The results indicate that the LKA module outperforms the ECA module. This finding highlights the importance of exploring different attention mechanisms and their impact on segmentation performance. To evaluate the effectiveness of the proposed method, we conduct experiments on benchmark datasets, including Stanford and Cityscapes, as well as the newly introduced WildPASS and DensPASS datasets. Based on our experiments, the proposed method achieved state-of-the-art results including mIoU 85.79 and 82.25 for the Stanford dataset, and the Cityscapes dataset, respectively. The results demonstrate that our proposed method performs well on these datasets, achieving state-of-the-art results with high segmentation accuracy.

MLFE‐UNet: Multi‐Level Feature Extraction Transformer‐Based UNet for Gastrointestinal Disease Segmentation

ABSTRACTAccurately segmenting gastrointestinal (GI) disease regions from Wireless Capsule Endoscopy images is essential for clinical diagnosis and survival prediction. However, challenges arise due to similar intensity distributions, variable lesion shapes, and fuzzy boundaries. In this paper, we propose MLFE‐UNet, an advanced fusion of CNN‐based transformers with UNet. Both the encoder and decoder utilize a multi‐level feature extraction (MLFA) CNN‐Transformer‐based module. This module extracts features from the input data, considering both global dependencies and local information. Furthermore, we introduce a multi‐level spatial attention (MLSA) block that functions as the bottleneck. It enhances the network's ability to handle complex structures and overlapping regions in feature maps. The MLSA block captures multiscale dependencies of tokens from the channel perspective and transmits them to the decoding path. A contextual feature stabilization block follows each transition to emulate lesion zones and facilitate segmentation guidelines at each phase. To address high‐level semantic information, we incorporate a computationally efficient spatial channel attention block. This is followed by a stabilization block in the skip connections, ensuring global interaction and highlighting important semantic features from the encoder to the decoder. To evaluate the performance of our proposed MLFE‐UNet, we selected common GI diseases, specifically bleeding and polyps. The dice coefficient scores obtained by MLFE‐UNet on the MICCAI 2017 (Red lesion) and CVC‐ClinicalDB data sets are 92.34% and 88.37%, respectively.

An attention mechanism-based lightweight UNet for musculoskeletal ultrasound image segmentation.

Accurate musculoseletal ultrasound (MSKUS) image segmentation is crucial for diagnosis and treatment planning. Compared with traditional segmentation methods, deploying deep learning segmentation methods that balance segmentation efficiency, accuracy, and model size on edge devices has greateradvantages. This paper aims to design a MSKUS image segmentation method that has fewer parameters, lower computation complexity and higher segmentationaccuracy. In this study, an attention mechanism-based lightweight UNet (AML-UNet) is designed to segment target muscle regions in MSKUS images. To suppress the transmission of redundant feature, Channel Reconstruction and Spatial Attention Module is designed in the encoding path. In addition, considering the inherent characteristic of MSKUS image, Multiscale Aggregation Module is developed to replace the skip connection architecture of U-Net. Deep supervision is also introduced to the decoding path to refine predicted masks gradually. Our method is evaluated on two MSKUS 2D-image segmentation datasets, including 3917 MSKUS and 1534 images respectively. In the experiments, a five-fold cross-validation method is adopted in ablation experiments and comparison experiments. In addition, Wilcoxon Signed-Rank Test and Bonferroni correction are employed to validate the significance level. 0.01 was used as the statistical significance level in ourpaper. AML-UNet yielded a mIoU of 84.17% and 90.14% on two datasets, representing a 3.38% ( ) and 3.48% ( ) over the Unext model. The number of parameters and FLOPs are only 0.21M and 0.96G, which are 1/34 and 1/29 of those in comparison withUNet. Our proposed model achieved superior results with fewer parameters while maintaining segmentation efficiency and accuracy compared to othermethods.

ERU-Net: A novel effective 2D residual neural network for brain tumors semantic segmentation from multimodal MRI

Brain tumor segmentation technology is crucial in diagnosing and treating MRI brain tumors. It aids doctors in locating and measuring tumors. It also plays a vital role in the planning and development of treatment and rehabilitation strategies. Recently, challenges have persisted despite the success achieved by deep learning-based methods in improving the segmentation accuracy of MRI brain tumor regions. These challenges are particularly prominent in small-scale tumor regions, where issues arise due to their diminutive size and the substantial variation between regions occupied by different tumor classes. Additionally, limitations in parameters and computational complexity contribute to these challenges. Thus, there remains considerable potential for enhancing these methods and increasing their efficacy. This paper presents an effective 2D residual neural network (2D ERU-Net) for segmenting MRI brain tumors. The proposed architecture consists of residual units in the encoder path to speed up training and convergence, and a deep supervision module (DSM) in the decoder path to address gradient-related issues and obtain high-resolution feature maps. Furthermore, a fusion weighted loss function, incorporating both weighted dice loss and weighted cross-entropy loss, is developed to solve the issues related to network convergence and data imbalance. The results of experiments on the reliable MRI brain tumor BraTS2019 dataset showed that the ERU-Net achieved a mean dice coefficient of 0.881, 0.882, and 0.891 and Hausdorff distances of 20.86 mm, 17.83 mm, and 13.35 mm for the tumor core, enhancing tumor, and whole tumor, respectively. Furthermore, ERU-Net demonstrates exceptional efficiency when compared to state-of-the-art methods.

Revealing in vivo broiler chicken growth state: Integrating CT imaging and deep learning for non-invasive reproductive phenotypic measurement

The measurement of body dimensions in broiler chickens has long been of great interest to the poultry industry, especially regarding reproductive phenotypes closely linked to breeding traits and the growth status of roosters. Existing measurement methods suffer from subjectivity, destructiveness, and various limitations. Therefore, we propose a novel method for the in vivo measurement of broiler chicken reproductive phenotype based on a three-dimensional medical imaging segmentation network. Leveraging our proposed FDTR medical imaging segmentation algorithm, this method achieves robust segmentation performance on a broiler chicken dataset. However, due to the morphological diversity and extreme class imbalance of broiler chicken reproductive phenotypes, we introduce a meta-learning paradigm and design a specialized decoder path. Ultimately, we establish relationships between the predicted results obtained from the segmentation network and the reproductive phenotypes to achieve phenotypic measurement. To evaluate this method, we collected and constructed the YF2023 broiler chicken reproductive phenotype dataset. Extensive cross-validation experiments demonstrate that the proposed method effectively measures the reproductive phenotypes of live roosters using computed tomography (CT) medical imaging. The proposed segmentation model achieves a mean fold dice similarity coefficient (DSC) of 0.6353, a mean fold Hausdorff distance (HD) of 11.84 mm, and a mean fold average volume distance (AVD) of 1.72 mm3. In addition, we evaluated our model’s generalization performance on unseen data, where the model achieved a DSC of 0.6825, an HD of 7.58 mm, and an AVD of 1.40 mm3. The predicted results exhibit a mean correlation coefficient of 66.18% with the weights of reproductive phenotype. This validates the enormous potential of the method in broiler chicken breeding and provides new insights and solutions for the accurate segmentation of broiler chicken reproductive phenotype. The code is available at https://github.com/Github-XKou/MetaFDTR.

A path splitting and pruning strategy on list decoder for PAC codes

AbstractRecently, Arıkan proposed a polarization‐adjusted convolutional (PAC) codes, demonstrating their superior error correction performance over polar codes at short block lengths. It was confirmed that PAC codes approached the optimal performance achievable with limited code length. This paper proposes a novel low‐complexity list decoding algorithm for PAC codes, incorporating path splitting and pruning strategies based on a set of highly reliable information bits. Simulation results reveal that the proposed algorithm significantly reduces sorting complexity and average list size, all while incurring negligible performance loss. Unlike previous pruning algorithms designed for polar codes, the proposed strategy eliminates the need to individually assess the reliability of decoding paths in each decoding process. Instead, the algorithm minimizes redundant decoding paths through a high‐reliability information bit set, constructed using Monte Carlo experiments.

Spatial attention guided cGAN for improved salient object detection

Recent research shows that Conditional Generative Adversarial Networks (cGANs) are effective for Salient Object Detection (SOD), a challenging computer vision task that mimics the way human vision focuses on important parts of an image. However, implementing cGANs for this task has presented several complexities, including instability during training with skip connections, weak generators, and difficulty in capturing context information for challenging images. These challenges are particularly evident when dealing with input images containing small salient objects against complex backgrounds, underscoring the need for careful design and tuning of cGANs to ensure accurate segmentation and detection of salient objects. To address these issues, we propose an innovative method for SOD using a cGAN framework. Our method utilizes encoder-decoder framework as the generator component for cGAN, enhancing the feature extraction process and facilitating accurate segmentation of the salient objects. We incorporate Wasserstein-1 distance within the cGAN training process to improve the accuracy of finding the salient objects and stabilize the training process. Additionally, our enhanced model efficiently captures intricate saliency cues by leveraging the spatial attention gate with global average pooling and regularization. The introduction of global average pooling layers in the encoder and decoder paths enhances the network's global perception and fine-grained detail capture, while the channel attention mechanism, facilitated by dense layers, dynamically modulates feature maps to amplify saliency cues. The generated saliency maps are evaluated by the discriminator for authenticity and gives feedback to enhance the generator's ability to generate high-resolution saliency maps. By iteratively training the discriminator and generator networks, the model achieves improved results in finding the salient object. We trained and validated our model using large-scale benchmark datasets commonly used for salient object detection, namely DUTS, ECSSD, and DUT-OMRON. Our approach was evaluated using standard performance metrics on these datasets. Precision, recall, MAE and Fβ score metrics are used to evaluate performance. Our method achieved the lowest MAE values: 0.0292 on the ECSSD dataset, 0.033 on the DUTS-TE dataset, and 0.0439 on the challenging and complex DUT-OMRON dataset, compared to other state-of-the-art methods. Our proposed method demonstrates significant improvements in salient object detection, highlighting its potential benefits for real-life applications.

A shape-supervised feature fusion U-Net for tubular structure segmentation

Accurate segmentation of tubular structures, such as blood vessels and bile ducts, is pivotal for clinical diagnosis and subsequent treatment. However, challenges arise from their unique structure and morphology. These structures are thin and elongated, and often exhibit complex branching patterns, making precise delineation difficult. Additionally, the intricate and often noisy backgrounds in medical images complicate the extraction of detailed and shape features. These challenges collectively hinder the effectiveness of automated segmentation methods. To address these issues, this paper introduces the shape-supervised feature fusion U-Net (SFU-Net), a novel approach that extends the traditional U-Net architecture. Our network incorporates a feature fusion module that effectively combines different semantic information in both the encoder and decoder paths, facilitating the capture of detailed geometric representations of tubular structures. Furthermore, a novel distance-based tubularity loss is designed to capture boundary information and supervise segmentation integrity by imposing shape constraints. We conducted comprehensive experiments on three tubular structure segmentation tasks in medical imaging, including the dilated biliary tree, hepatic vessel, and pulmonary artery. The experimental results illustrate that the proposed SFU-Net achieves the best performance in terms of accuracy and completeness, with Dice scores of 76.14%, 80.42%, and 83.79%, and HD95 values of 8.59 mm, 6.43 mm, and 4.31 mm across three datasets, respectively. Compared to traditional U-Net-based segmentation methods and other tubular segmentation approaches, our method exhibits superior performance, underscoring its effectiveness in medical image segmentation with tubular structures.

Flow field prediction in bed configurations: A parametric spatio-temporal convolutional autoencoder approach

The use of deep learning methods for modeling fluid flow has drawn a lot of attention in the past few years. Here we present a data-driven reduced-order model (ROM) for predicting flow fields in a bed configuration of hot particles. The ROM consists of a parametric spatio-temporal convolutional autoencoder. The neural network architecture comprises two main components. The first part resolves the spatial and temporal dependencies present in the input sequence, while the second part of the architecture is responsible for predicting the solution at the subsequent timestep based on the information gathered from the preceding part. We also propose the utilization of a post-processing non-trainable output layer following the decoding path to incorporate the physical knowledge, e.g. no-slip condition, into the prediction. The ROM is evaluated by comparing its predicted solution with the high-fidelity counterpart. In addition, proper orthogonal decomposition (POD) is employed to systematically analyze and compare the dominant structures present in both sets of solutions. The assessment of the ROM for a bed configuration with variable particle temperature showed accurate results at a fraction of the computational cost required by traditional numerical simulation methods.

Segmentation and visualization of Retinal Detachment lesions through Retinal fundus images

Medical image segmentation is a prominent task in medical image processing and a fundamental aspect of medical image analysis. However, accurate segmentation is exceedingly challenging due to the variations in size, shape, and location of the lesions. U-Net is well-linked in the domain of medical image segmentation, but it fails to fully exploit the advantageous characteristics of the channel or leverage contextual information. This article presents a novel, improved MultiResU-Net framework called DCA-MultiResU-Net to minimize the loss of crucial features and also to enhance the efficiency of end-to-end image segmentation by utilizing the full advantages of U-Net, Res2Net, Channel attention mechanism, Dilated convolution layers, and MultiResUNet models. In contrast to the typical convolutions of U-Net in the encoder path, the dilated convolution modules are inserted into the MultiRes blocks to extract and concatenate the multi-scale features. Furthermore, a channel attention mechanism is employed in the bottom portion of the “U” shaped network and the decoder path to fuse the information from receptive fields of varying sizes. Afterward, the virtual reality visualization technology was implemented for the 3-D visualization of segmented RD lesions. The proposed segmentation model was evaluated on four online databases: Retinal Image Bank, RIADD, Kaggle, and Cataract Image Dataset or GitHub. The performance outcomes of this work were measured using standard evaluation indexes such as Dice similarity coefficient, Intersection over the union, F1-score, and Loss function score of 90.76%, 88.42%, 93.28%, and 0.1641, respectively. The experimental findings indicate that the DA-MultiResU-Net framework achieved superior performance and generalization capabilities while utilizing 6.76% fewer parameters than the U-Net model.

Chaotic Phase Modulation Direct-Sequence Spread Spectrum-Assisted Adaptive Serial Cancellation List Decoding Method for Underwater Acoustic Communication

Addressing the challenges of high decoding latency, reduced spectral efficiency, and substantial storage requirements in a Cyclic Redundancy Check (CRC)Aided Successive Cancellation List (CA-SCL) polar decoder, this paper proposes a chaotic phase modulation direct-sequence spread spectrum (CPMDSSS)-assisted adaptive serial cancellation list decoding method for underwater acoustic communication. The method involves segmenting the information bits to be transmitted, computing CRC for each segment, and mapping all CRCs to a CPMDSSS, which is then modulated onto the pilot subcarriers of underwater acoustic orthogonal frequency-division multiplexing (OFDM) to increase spectral efficiency. At the receiving end, CRCs are obtained by demodulating the CPMDSSS to verify the segmented information and adaptively select the number of decoding paths. The theoretical analysis and simulation results demonstrate that compared to CA-SCL, the proposed method effectively reduces the required storage units and improves spectral efficiency, with an average reduction of approximately 80% in the decoding paths. Sea trials further indicate that the proposed method reduces the average decoding paths by approximately 71% and decreases the average decoding delay by approximately 64% compared to CA-SCL.

Cross-patch feature interactive net with edge refinement for retinal vessel segmentation

Retinal vessel segmentation based on deep learning is an important auxiliary method for assisting clinical doctors in diagnosing retinal diseases. However, existing methods often produce mis-segmentation when dealing with low contrast images and thin blood vessels, which affects the continuity and integrity of the vessel skeleton. In addition, existing deep learning methods tend to lose a lot of detailed information during training, which affects the accuracy of segmentation. To address these issues, we propose a novel dual-decoder based Cross-patch Feature Interactive Net with Edge Refinement (CFI-Net) for end-to-end retinal vessel segmentation. In the encoder part, a joint refinement down-sampling method (JRDM) is proposed to compress feature information in the process of reducing image size, so as to reduce the loss of thin vessels and vessel edge information during the encoding process. In the decoder part, we adopt a dual-path model based on edge detection, and propose a Cross-patch Interactive Attention Mechanism (CIAM) in the main path to enhancing multi-scale spatial channel features and transferring cross-spatial information. Consequently, it improve the network’s ability to segment complete and continuous vessel skeletons, reducing vessel segmentation fractures. Finally, the Adaptive Spatial Context Guide Method (ASCGM) is proposed to fuse the prediction results of the two decoder paths, which enhances segmentation details while removing part of the background noise. We evaluated our model on two retinal image datasets and one coronary angiography dataset, achieving outstanding performance in segmentation comprehensive assessment metrics such as AUC and CAL. The experimental results showed that the proposed CFI-Net has superior segmentation performance compared with other existing methods, especially for thin vessels and vessel edges. The code is available at https://github.com/kita0420/CFI-Net.

S2DA-Net: Spatial and spectral-learning double-branch aggregation network for liver tumor segmentation in CT images

Accurate liver tumor segmentation is crucial for aiding radiologists in hepatocellular carcinoma evaluation and surgical planning. While convolutional neural networks (CNNs) have been successful in medical image segmentation, they face challenges in capturing long-term dependencies among pixels. On the other hand, Transformer-based models demand a high number of parameters and involve significant computational costs. To address these issues, we propose the Spatial and Spectral-learning Double-branched Aggregation Network (S2DA-Net) for liver tumor segmentation. S2DA-Net consists of a double-branched encoder and a decoder with a Group Multi-Head Cross-Attention Aggregation (GMCA) module, Two branches in the encoder consist of a Fourier Spectral-learning Multi-scale Fusion (FSMF) branch and a Multi-axis Aggregation Hadamard Attention (MAHA) branch. The FSMF branch employs a Fourier-based network to learn amplitude and phase information, capturing richer features and detailed information without introducing an excessive number of parameters. The FSMF branch utilizes a Fourier-based network to capture amplitude and phase information, enriching features without introducing excessive parameters. The MAHA branch incorporates spatial information, enhancing discriminative features while minimizing computational costs. In the decoding path, a GMCA module extracts local information and establishes long-term dependencies, improving localization capabilities by amalgamating features from diverse branches. Experimental results on the public LiTS2017 liver tumor datasets show that the proposed segmentation model achieves significant improvements compared to the state-of-the-art methods, obtaining dice per case (DPC) 69.4 % and global dice (DG) 80.0 % for liver tumor segmentation on the LiTS2017 dataset. Meanwhile, the pre-trained model based on the LiTS2017 datasets obtain, DPC 73.4 % and an DG 82.2 % on the 3DIRCADb dataset.

Multi-scale adversarial learning with difficult region supervision learning models for primary tumor segmentation

Objective. Recently, deep learning techniques have found extensive application in accurate and automated segmentation of tumor regions. However, owing to the variety of tumor shapes, complex types, and unpredictability of spatial distribution, tumor segmentation still faces major challenges. Taking cues from the deep supervision and adversarial learning, we have devised a cascade-based methodology incorporating multi-scale adversarial learning and difficult-region supervision learning in this study to tackle these challenges. Approach. Overall, the method adheres to a coarse-to-fine strategy, first roughly locating the target region, and then refining the target object with multi-stage cascaded binary segmentation which converts complex multi-class segmentation problems into multiple simpler binary segmentation problems. In addition, a multi-scale adversarial learning difficult supervised UNet (MSALDS-UNet) is proposed as our model for fine-segmentation, which applies multiple discriminators along the decoding path of the segmentation network to implement multi-scale adversarial learning, thereby enhancing the accuracy of network segmentation. Meanwhile, in MSALDS-UNet, we introduce a difficult region supervision loss to effectively utilize structural information for segmenting difficult-to-distinguish areas, such as blurry boundary areas. Main results. A thorough validation of three independent public databases (KiTS21, MSD’s Brain and Pancreas datasets) shows that our model achieves satisfactory results for tumor segmentation in terms of key evaluation metrics including dice similarity coefficient, Jaccard similarity coefficient, and HD95. Significance. This paper introduces a cascade approach that combines multi-scale adversarial learning and difficult supervision to achieve precise tumor segmentation. It confirms that the combination can improve the segmentation performance, especially for small objects (our codes are publicly availabled on https://zhengshenhai.github.io/).

Philosophical Critique and Decoding Path of Rosa's New Alienation

The theory of alienation occupies a very important position in Marx's theoretical system. In the Economic and Philosophic Manuscripts of 1844, Marx criticizes classical political economy in England starting from alienated labor and puts forward four aspects of alienated labor. The Frankfurt School further developed Marx's theory of alienation and conducted comprehensive investigations on technological alienation, cultural alienation, consumer alienation, and other aspects of modern society's culture, politics, and art. In the new turn of late modern society, the fourth generation Frankfurt School scholar Hartmut Rosa, starting from time analysis, analyzes the growth logic and dynamic stability of modern society's overall crisis through the theory of social acceleration critique, and proposes the social pathologies of "acceleration" and the solution of "resonance", attempting to achieve a better life for people in the present era through the adaptive mode of resonance theory. Based on Hartmut Rosa's works and related research, this article explores the theoretical origins of Rosa's theory of social acceleration critique, the historical background and ideological elements, elaborates on Rosa's analysis of the phenomenon of social acceleration, the new forms of alienation under the background of accelerated society, and the resonant solutions to the problem of new alienation. Finally, this article examines the theoretical value and shortcomings of his theory.

Concurrent Learning Approach for Estimation of Pelvic Tilt from Anterior-Posterior Radiograph.

Accurate and reliable estimation of the pelvic tilt is one of the essential pre-planning factors for total hip arthroplasty to prevent common post-operative complications such as implant impingement and dislocation. Inspired by the latest advances in deep learning-based systems, our focus in this paper has been to present an innovative and accurate method for estimating the functional pelvic tilt (PT) from a standing anterior-posterior (AP) radiography image. We introduce an encoder-decoder-style network based on a concurrent learning approach called VGG-UNET (VGG embedded in U-NET), where a deep fully convolutional network known as VGG is embedded at the encoder part of an image segmentation network, i.e., U-NET. In the bottleneck of the VGG-UNET, in addition to the decoder path, we use another path utilizing light-weight convolutional and fully connected layers to combine all extracted feature maps from the final convolution layer of VGG and thus regress PT. In the test phase, we exclude the decoder path and consider only a single target task i.e., PT estimation. The absolute errors obtained using VGG-UNET, VGG, and Mask R-CNN are 3.04 ± 2.49, 3.92 ± 2.92, and 4.97 ± 3.87, respectively. It is observed that the VGG-UNET leads to a more accurate prediction with a lower standard deviation (STD). Our experimental results demonstrate that the proposed multi-task network leads to a significantly improved performance compared to the best-reported results based on cascaded networks.

ETUNet:Exploring efficient transformer enhanced UNet for 3D brain tumor segmentation

Medical image segmentation is a crucial topic in medical image processing. Accurately segmenting brain tumor regions from multimodal MRI scans is essential for clinical diagnosis and survival prediction. However, similar intensity distributions, variable tumor shapes, and fuzzy boundaries pose severe challenges for brain tumor segmentation. Traditional segmentation networks based on UNet struggle to establish explicit long-range dependencies from the feature space due to the limitations of the CNN receptive field. This is particularly crucial for dense prediction tasks such as brain tumor segmentation. Recent works have incorporated the powerful global modeling capability of Transformer into UNet to achieve more precise segmentation results. Nevertheless, these methods encounter some issues: (1) the global information is often modeled by simply stacking Transformer layers for a specific module, resulting in high computational complexity and underutilization of the potential of the UNet architecture; (2) the rich boundary information of tumor subregions in multi-scale features is often overlooked. Motivated by these challenges, we propose an advanced fusion of Transformer with UNet by reexamining the core three parts (encoder, bottleneck, and skip connections). Firstly, we introduce a CNN-Transformer module in the encoder to replace the traditional CNN module, enabling the capture of deep spatial dependencies from input images. To address high-level semantic information, we incorporate a computationally efficient spatial-channel attention layer in the bottleneck for global interaction, highlighting important semantic features from the encoder path output. For irregular lesions, we fuse the multi-scale features from the encoder output and the decoder features in the skip connections by calculating cross-attention. This adaptive querying of valuable information from multi-scale features enhances the boundary localization ability of the decoder path and suppresses redundant features with low correlation. Compared to existing methods, our model further enhances the learning capacity of the overall UNet architecture while maintaining low computational complexity. Experimental results on the BraTS2018 and BraTS2020 datasets for brain tumor segmentation tasks demonstrate that our model achieves comparable or superior results compared to recent CNN or Transformer-based models. The average DSC and HD95 on the two datasets are 0.854, 6.688, and 0.862, 5.455 respectively. At the same time, our model achieves optimal segmentation of Enhancing tumors, showcasing the effectiveness of our method. Our code will be made publicly available at https://github.com/wzhangck/ETUnet.