Binary Cross-entropy Loss Research Articles

Transfer Learning Approaches for Brain Metastases Screenings

Background: In this study, we examined the effectiveness of transfer learning in improving automatic segmentation of brain metastases on magnetic resonance imaging scans, with potential applications in preventive exams and remote diagnostics. Methods: We trained three deep learning models on a public dataset from the ASNR-MICCAI Brain Metastasis Challenge 2024, fine-tuned them on a small private dataset, and compared their performance to models trained from scratch. Results: Results showed that models using transfer learning performed better than scratch-trained models, though the improvement was not statistically substantial. The custom Tversky and Binary Cross-Entropy loss function helped manage class imbalance and reduce false negatives, limiting missed tumor regions. Medical experts noted that, while fine-tuned models worked well with larger, well-defined tumors, they struggled with tiny, scattered tumors in complex cases. Conclusions: This study highlights the potential of transfer learning and tailored loss functions in medical imaging, while also pointing out the models’ limitations in detecting very small tumors in challenging cases.

Anatomical landmark detection on bi-planar radiographs for predicting spinopelvic parameters.

Accurate landmark detection is essential for precise analysis of anatomical structures, supporting diagnosis, treatment planning, and monitoring in patients with spinal deformities. Conventional methods rely on laborious landmark identification by medical experts, which motivates automation. The proposed deep learning pipeline processes bi-planar radiographs to determine spinopelvic parameters and Cobb angles without manual supervision. The dataset used for training and evaluation consisted of 555 bi-planar radiographs from un-instrumented patients, which were manually annotated by medical professionals. The pipeline performed a pre-processing step to determine regions of interest, including the cervical spine, thoracolumbar spine, sacrum, and pelvis. For each ROI, a segmentation network was trained to identify vertebral bodies and pelvic landmarks. The U-Net architecture was trained on 455 bi-planar radiographs using binary cross-entropy loss. The post-processing algorithm determined spinal alignment and angular parameters based on the segmentation output. We evaluated the pipeline on a test set of 100 previously unseen bi-planar radiographs, using the mean absolute difference between annotated and predicted landmarks as the performance metric. The spinopelvic parameter predictions of the pipeline were compared to the measurements of two experienced medical professionals using intraclass correlation coefficient(ICC) and mean absolute deviation (MAD). The pipeline was able to successfully predict the Cobb angles in 61% of all test cases and achieved mean absolute differences of 3.3° (3.6°) and averaged ICC of 0.88. For thoracic kyphosis, lumbar lordosis, sagittal vertical axis, sacral slope, pelvic tilt, and pelvic incidence, the pipeline produced reasonable outputs in 69%, 58%, 86%, 85%, 84%, and 84% of the cases. The MAD was 5.6° (7.8°), 4.7° (4.3°), 2.8mm (3.0mm), 4.5° (7.2°), 1.8° (1.8°), and 5.3° (7.7°), while the ICC was measured at 0.69, 0.82, 0.99, 0.61, 0.96, and 0.70, respectively. Despite limitations in patients with severe pathologies and high BMI, the pipeline automatically predicted coronal and sagittal spinopelvic parameters, which has the potential to simplify clinical routines and large-scale retrospective data analysis.

Leveraging survival analysis in cost-aware deepnet for efficient hard drive failure prediction

AbstractMany real-world datasets, such as those used for failure and anomaly detection, are severely imbalanced, with a relatively small number of failed instances compared to the number of normal instances. To address these issues, this paper leverages the Backblaze hard disk drives (HDDs) data and makes several contributions to hard drive failure prediction. This research explores 1D convolutional neural networks (CNN) to utilize the sequential nature of hard drive sensor data. The performance of 1D CNN models is compared to traditional machine learning (ML) algorithms, such as the synthetic minority over-sampling technique (SMOTE) and weighted logistic regression (WLR), demonstrating superior results, suggesting the potential effectiveness of the proposed approaches. In addition to these efforts, this paper aims to provide a comprehensive understanding of hard drive longevity and the critical factors contributing to their eventual failure through survival analysis. The 1D CNN models employ weighted binary cross-entropy (WCE) loss and modified focal loss (MFL) functions to manage class imbalanced issues commonly observed in hard drive data. The findings suggest that 1D CNN models outperform traditional ML models, with regularization techniques like dropout and early stopping proving effective in controlling overfitting. Notably, the 1D CNN model with WCE loss demonstrated the best overall performance with a $$G_{mean}$$ G mean of 0.692, successfully maximizing the FDR without increasing the FAR. In parallel, the research also employs Cox regression to identify key SMART parameters influencing drive failure. The high concordance index (c-index) of the Cox model (0.958) adds confidence to the insights derived. The research thus sets a solid groundwork for data center management strategies, with a future focus on practical implementation and evaluation of these findings.

Performance Analysis of Transfer Learning Models for Identifying AI-Generated and Real Images

This study aims to analyze and compare the performance of three transfer learning methods, namely InceptionV3, VGG16, and DenseNet121, in detecting AI-generated and real images. The background of this research is the unknown performance of transfer learning methods for detecting AI-generated and real images. This study introduces innovation by conducting 54 experiments involving three types of transfer learning, three dataset split ratios (60:40, 70:30, and 80:20), three optimizers (Adam, SGD, and RMSprop), two numbers of epochs (20 and 50), and the addition of dense and flatten layers during fine tuning. Performance evaluation was conducted using binary cross entropy loss and confusion matrix. This research provides significant benefits in determining the most effective transfer learning model for detecting AI-generated and real images and offers practical guidance for further development. The results show that the InceptionV3 model with the Adam optimizer, an 80:20 split ratio, and 20 epochs achieved the highest accuracy of 84.26%, with a loss of 39.54%, precision of 81.33%, recall of 82.43%, and an F1-Score of 81.88%.

Ensemble graph neural networks for fake news detection using user engagement and text features

In this digital world with massive information sharing, spreading misinformation is relatively easy, but its consequences are really enormous. Detecting fake news is a critical challenge in today's digital age, where misinformation can spread rapidly and influence public opinion. This paper proposes an ensemble model (EGNN) that leverages Graph Neural Networks (GNNs) and text features to enhance the accuracy of fake news detection. The model is designed to operate effectively even when labeled data is scarce by utilizing a substantial amount of unlabeled data. We employ multiple GNNs, including a standard GNN, a Graph Attention Network (GAT), and a Bidirectional GCN (BiGCN), to capture user engagement patterns and social context. Additionally, text embeddings are extracted using spaCy, BERT, and a combination of spaCy with user profile information. These embeddings are then classified using a neural network, and the final detection is performed using ensemble techniques such as Majority Voting, Weighted Average, and Stacking. To address class imbalance, we incorporate focal loss alongside the traditional binary cross-entropy loss. Experimental results demonstrate that our proposed EGNN model significantly improves the detection of fake news, providing a robust solution for this pervasive problem.

F.sh: A 3D Recurrent Residual Attention U-Net for Automated Multiple Sclerosis Lesion Segmentation

Multiple sclerosis (MS) is an autoimmune disease affecting the central nervous system, characterized by lesions in the brain and spinal cord. Accurate detection and localization of these lesions on MRI scans is crucial for diagnosis and monitoring disease progression. Manual segmentation is time-consuming and prone to inter-rater variability. This study proposes F.sh (3DR2AUNet), a novel deep learning architecture for automated MS lesion segmentation. F.sh combines 3D recurrent residual blocks, attention gates, and the U-Net structure to effectively capture lesion features. The model was trained and evaluated using a comprehensive approach, including patch-based preprocessing, data augmentation, and a composite loss function combining Binary Cross-Entropy and 3D Dice Loss. Experimental results demonstrate the superior performance of F.sh compared to baseline methods, achieving a Dice score of 0.92. The proposed approach has the potential to assist radiologists in the accurate and efficient assessment of MS lesion burden.

Multi-Scale Feature Attention Fusion for Image Splicing Forgery Detection

Image splicing is a widely occurrence image tampering technology. With the rapid development of digital image processing technology, detecting image splicing forgery has become significantly challenging. Although various methods have been devised to identify such tampered images, existing approaches have not achieved optimal performance due to limitations in effectively leveraging feature maps of different scales. To address this issue, we propose a novel method for image splicing forgery detection called multi-scale feature attention fusion network (MFAF-Net). We propose a multi-scale atrous feature attention (MAFA) module designed to capture rich contextual features for multi-scale high-level feature fusion. Additionally, we present the multi-branch attention mechanism (MBAM) module to fuse contextual information from various branches for low-level features. This integration enhances the capability of low-level features to produce more refined pixel-level attention. We employ the weighted binary cross-entropy loss and dice loss in the MFAF-Net to overcome the imbalance between positive and negative samples. Extensive experiments demonstrate that the proposed MFAF-Net outperforms state-of-the-art methods. Robustness experiments also show our model exhibits image splicing forgery detection robustness under common attacks.

Improving Effectiveness by Reducing Overconfidence in Large Catalogue Sequential Recommendation with gBCE loss

A large catalogue size is one of the central challenges in training recommendation models: a large number of items makes them memory and computationally inefficient to compute scores for all items during training, forcing these models to deploy negative sampling. However, negative sampling increases the proportion of positive interactions in the training data, and therefore, models trained with negative sampling tend to overestimate the probabilities of positive interactions – a phenomenon we call overconfidence . While the absolute values of the predicted scores/probabilities are not important for the ranking of retrieved recommendations, overconfident models may fail to estimate nuanced differences in the top-ranked items, resulting in degraded performance. In this paper, we show that overconfidence explains why the popular SASRec model underperforms when compared to BERT4Rec. This is contrary to the BERT4Rec authors’ explanation that the difference in performance is due to the bi-directional attention mechanism. To mitigate overconfidence, we propose a novel Generalised Binary Cross-Entropy Loss function (gBCE) and theoretically prove that it can mitigate overconfidence. We further propose the gSASRec model, an improvement over SASRec that deploys an increased number of negatives and the gBCE loss. Through detailed experiments on three datasets, we show that gSASRec does not exhibit the overconfidence problem. As a result, gSASRec can outperform BERT4Rec (e.g. +9.47% NDCG on the MovieLens-1M dataset), while requiring less training time (e.g. -73% training time on MovieLens-1M). Moreover, in contrast to BERT4Rec, gSASRec is suitable for large datasets that contain more than 1 million items. Finally, we show how addressing overconfidence can improve model calibration – the ability of a model to predict actual interaction probabilities accurately. By applying gBCE to the SASRec model on MovieLens-1M dataset, we reduce the models’ expected calibration error by 98.9% (from 0.966 to 0.01).

Method for Augmenting Side-Scan Sonar Seafloor Sediment Image Dataset Based on BCEL1-CBAM-INGAN.

In this paper, a method for augmenting samples of side-scan sonar seafloor sediment images based on CBAM-BCEL1-INGAN is proposed, aiming to address the difficulties in acquiring and labeling datasets, as well as the insufficient diversity and quantity of data samples. Firstly, a Convolutional Block Attention Module (CBAM) is integrated into the residual blocks of the INGAN generator to enhance the learning of specific attributes and improve the quality of the generated images. Secondly, a BCEL1 loss function (combining binary cross-entropy and L1 loss functions) is introduced into the discriminator, enabling it to focus on both global image consistency and finer distinctions for better generation results. Finally, augmented samples are input into an AlexNet classifier to verify their authenticity. Experimental results demonstrate the excellent performance of the method in generating images of coarse sand, gravel, and bedrock, as evidenced by significant improvements in the Frechet Inception Distance (FID) and Inception Score (IS). The introduction of the CBAM and BCEL1 loss function notably enhances the quality and details of the generated images. Moreover, classification experiments using the AlexNet classifier show an increase in the recognition rate from 90.5% using only INGAN-generated images of bedrock to 97.3% using images augmented using our method, marking a 6.8% improvement. Additionally, the classification accuracy of bedrock-type matrices is improved by 5.2% when images enhanced using the method presented in this paper are added to the training set, which is 2.7% higher than that of the simple method amplification. This validates the effectiveness of our method in the task of generating seafloor sediment images, partially alleviating the scarcity of side-scan sonar seafloor sediment image data.

A dual-channel multimodal sentiment analysis framework based on three-way decision

In the exploration of human–computer interaction, accurately identifying and understanding the user’s sentimental state helps enhance the user experience. Multimodal sentiment analysis aims to identify sentiments through various signals. However, inconsistencies in sentiments between text and visual–audio signals pose a challenge, as models often struggle to capture these differences adequately. To alleviate this issue, we propose a dual-channel sentiment analysis framework based on three-way decision. This framework includes a multimodal model, a visual–audio model, and a three-way decision module. Specifically, the multimodal model and visual–audio model generate predictions, and the three-way decision module selects an appropriate prediction. We improve the binary cross-entropy loss function for the visual–audio model to integrate the three-way decision module better and improve sentiment prediction accuracy. Additionally, we introduce an adversarial training task to ensure differences in predictions between the two models, thereby enhancing the influence of the visual–audio model. Experimental results on four public datasets demonstrate that our framework significantly improves the accuracy of sentiment recognition and reduces the average decision time.

Deep Learning-driven Automatic Nuclei Segmentation of Label-free Live Cell Chromatin-sensitive Partial Wave Spectroscopic Microscopy Imaging.

Chromatin-sensitive Partial Wave Spectroscopic (csPWS) microscopy offers a non-invasive glimpse into the mass density distribution of cellular structures at the nanoscale, leveraging the spectroscopic information. Such capability allows us to analyze the chromatin structure and organization and the global transcriptional state of the cell nuclei for the study of its role in carcinogenesis. Accurate segmentation of the nuclei in csPWS microscopy images is an essential step in isolating them for further analysis. However, manual segmentation is error-prone, biased, time-consuming, and laborious, resulting in disrupted nuclear boundaries with partial or over-segmentation. Here, we present an innovative deep-learning-driven approach to automate the accurate nuclei segmentation of label-free live cell csPWS microscopy imaging data. Our approach, csPWS-seg, harnesses the Convolutional Neural Networks-based U-Net model with an attention mechanism to automate the accurate cell nuclei segmentation of csPWS microscopy images. We leveraged the structural, physical, and biological differences between the cytoplasm, nucleus, and nuclear periphery to construct three distinct csPWS feature images for nucleus segmentation. Using these images of HCT116 cells, csPWS-seg achieved superior performance with a median Intersection over Union (IoU) of 0.80 and a Dice Similarity Coefficient (DSC) score of 0.88. The csPWS-seg overcame the segmentation performance over the baseline U-Net model and another attention-based model, SE-U-Net, marking a significant improvement in segmentation accuracy. Further, we analyzed the performance of our proposed model with four loss functions: binary cross-entropy loss, focal loss, dice loss, and Jaccard loss. The csPWS-seg with focal loss provided the best results compared to other loss functions. The automatic and accurate nuclei segmentation offered by the csPWS-seg not only automates, accelerates, and streamlines csPWS data analysis but also enhances the reliability of subsequent chromatin analysis research, paving the way for more accurate diagnostics, treatment, and understanding of cellular mechanisms for carcinogenesis.

RADAP: A Robust and Adaptive Defense Against Diverse Adversarial Patches on face recognition

Face recognition (FR) systems powered by deep learning have become widely used in various applications. However, they are vulnerable to adversarial attacks, especially those based on local adversarial patches that can be physically applied to real-world objects. In this paper, we propose RADAP, a robust and adaptive defense mechanism against diverse adversarial patches in both closed-set and open-set FR systems. RADAP employs innovative techniques, such as FCutout and F-patch, which use Fourier space sampling masks to improve the occlusion robustness of the FR model and the performance of the patch segmenter. Moreover, we introduce an edge-aware binary cross-entropy (EBCE) loss function to enhance the accuracy of patch detection. We also present the split and fill (SAF) strategy, which is designed to counter the vulnerability of the patch segmenter to complete white-box adaptive attacks. We conduct comprehensive experiments to validate the effectiveness of RADAP, which shows significant improvements in defense performance against various adversarial patches, while maintaining clean accuracy higher than that of the undefended Vanilla model.

Hybrid Feature Mammogram Analysis: Detecting and Localizing Microcalcifications Combining Gabor, Prewitt, GLCM Features, and Top Hat Filtering Enhanced with CNN Architecture.

Breast cancer is a prevalent malignancy characterized by the uncontrolled growth of glandular epithelial cells, which can metastasize through the blood and lymphatic systems. Microcalcifications, small calcium deposits within breast tissue, are critical markers for early detection of breast cancer, especially in non-palpable carcinomas. These microcalcifications, appearing as small white spots on mammograms, are challenging to identify due to potential confusion with other tissues. This study hypothesizes that a hybrid feature extraction approach combined with Convolutional Neural Networks (CNNs) can significantly enhance the detection and localization of microcalcifications in mammograms. The proposed algorithm employs Gabor, Prewitt, and Gray Level Co-occurrence Matrix (GLCM) kernels for feature extraction. These features are input to a CNN architecture designed with maxpooling layers, Rectified Linear Unit (ReLU) activation functions, and a sigmoid response for binary classification. Additionally, the Top Hat filter is used for precise localization of microcalcifications. The preprocessing stage includes enhancing contrast using the Volume of Interest Look-Up Table (VOI LUT) technique and segmenting regions of interest. The CNN architecture comprises three convolutional layers, three ReLU layers, and three maxpooling layers. The training was conducted using a balanced dataset of digital mammograms, with the Adam optimizer and binary cross-entropy loss function. Our method achieved an accuracy of 89.56%, a sensitivity of 82.14%, and a specificity of 91.47%, outperforming related works, which typically report accuracies around 85-87% and sensitivities between 76 and 81%. These results underscore the potential of combining traditional feature extraction techniques with deep learning models to improve the detection and localization of microcalcifications. This system may serve as an auxiliary tool for radiologists, enhancing early detection capabilities and potentially reducing diagnostic errors in mass screening programs.

Balancing accuracy and efficiency: A lightweight deep learning model for COVID 19 detection

The novel coronavirus disease in human was detected in Wuhan, China and spread rapidly across countries each time mutating itself as a new variant. It has impacted not only the patients but also medical infrastructure a lot. Several artificial intelligence-based systems for detection of covid-19 in chest images have been proposed. This study aims to develop a deep learning-based approach that efficiently detect Covid-19 using chest Computed Tomography (CT) images. In this work, we propose an efficient and lightweight deep-learning architecture based on the concept of a densely connected network for Covid-19 detection. The proposed architecture is designed by stacking dense modules in which each layer is directly connected to the other layers, with very fewer number of neurons at each layer. The dense connection is used to improve the learning capability of the proposed model and the fewer number of neurons make network computationally efficient. CT images for covid-19 detection are effectively used for the detection of Covid-19 patients. We train and test our model on SARS-CoV-2 CT-scan datasets. The experimental results are evaluated on accuracy, sensitivity and F1 score. The Adam optimizer with binary cross entropy loss function gives 97.2% accuracy with 0.98 sensitivity, 0.97 precision and 0.97 F1 score. The model achieves maximum accuracy of 88% at precision 0.97%, specificity 0.82 and F1 score 0.89 with Covid-CT dataset. The proposed model is compared with other state-of-the-art methods. The results of the proposed methods and its comparison shows acceptable level of performance on chest CT images.

Incorporating long-tail data in complex backgrounds for visual surface defect detection in PCBs

AbstractHigh-quality printed circuit boards (PCBs) are essential components in modern electronic circuits. Nevertheless, most of the existing methods for PCB surface defect detection neglect the fact that PCB surface defects in complex backgrounds are prone to long-tailed data distributions, which in turn affects the effectiveness of defect detection. Additionally, most of the existing methods ignore the intra-scale features of defects and do not utilize auxiliary supervision strategies to improve the detection performance of the network. To tackle these issues, we propose a lightweight long-tailed data mining network (LLM-Net) for identifying PCB surface defects. Firstly, the proposed Efficient Feature Fusion Network (EFFNet) is applied to embed intra-scale feature associations and multi-scale features of defects into LLM-Net. Next, an auxiliary supervision method with a soft label assignment strategy is designed to help LLM-Net learn more accurate defect features. Finally, the issue of inadequate tail data detection is addressed by employing the devised Binary Cross-Entropy Loss Rank Mining method (BCE-LRM) to identify challenging samples. The performance of LLM-Net was evaluated on a homemade dataset of PCB surface soldering defects, and the results show that LLM-Net achieves the best accuracy of mAP@0.5 for the evaluation metric of the COCO dataset, and it has a real-time inference speed of 188 frames per second (FPS).

UCSwin‐UNet model for medical image segmentation based on cardiac haemangioma

AbstractCardiac hemangioma is a rare benign tumour that presents diagnostic challenges due to its variable clinical symptoms, imaging features, and locations. This study proposes a novel segmentation method based on a Convolutional Neural Network (CNN) and Transformer integration, with Swin‐UNet as the core model. We incorporated a U‐shaped convolutional neural network block into the original jump connection of Swin‐UNet. The Binary Cross Entropy Loss (BCE Loss) algorithm was added, and the learning rate decay algorithm was modified to select the appropriate one by comparing loss values. This paper utilizes the publicly available cardiac angioma dataset in AI Studio, consisting of 215 images for training and testing. To evaluate the effectiveness of the proposed model, this paper demonstrates its optimality through ablation experiments and comparisons with other mainstream models. The comparison experiments show that this model improves Dice by approximately 12%, HD95 by approximately 4.7 mm, Accuracy by approximately 6.1%, and F1 score by 0.11 compared to models such as UNet, UNet++, and Deeplabv3+, etc. For the recently proposed SOTO models, such as TransUNet, Swin‐UNet, and MultiResUnet, the Dice score improved by about 1.2%, HD95 reduced by about 1mm, Accuracy improved by about 0.3%, and F1 score improved by 0.015.

Imbalanced segmentation for abnormal cotton fiber based on GAN and multiscale residual U-Net

The scale of white foreign fibers in bobbin yarn is small, resulting in multiple types of data imbalance in the dataset. These imbalances include a severe imbalance of foreign fiber pixels compared to background pixels and an imbalance in the size target scale. Consequently, conventional semantic segmentation networks struggle to segment these fibers effectively. First, in tackling the scarcity of white foreign fiber instances within bobbin yarn samples, this research utilizes original foreign fiber images to train the DCGAN and generate adequate training samples. Secondly, a multiscale residual U-Net is constructed to extract foreign fiber features from different scales. The network is encouraged to learn semantic features at each scale and each layer of the decoding stage. This overcomes the problem of scale imbalance in the foreign fiber dataset and enhances the model's capability to extract weak semantic information from small targets. Thirdly, a weighted binary cross-entropy loss function is integrated into the network's training phase to rectify the class imbalance and refine segmentation performance. This function adjusts the weighting of foreign fiber pixel data, thereby addressing the disproportionate distribution between foreign fibers and background pixels within the dataset. Finally, the proposed method is experimentally validated using a dataset of white foreign fibers. The experimental results show that the proposed method achieves better results in the critical evaluation metrics, as evidenced by the accuracy of 97.52 %, the MIoU of 95.26 %, the DICE coefficient of 81.29 %, and the F1 Score of 84.92 %. These statistics demonstrate the method's efficacy in achieving high-precision segmentation of white foreign fibers.

SAST: A self-attention based method for skill translation in T-shaped expert finding

Nowadays, organizations are seeking professionals who can both excel in their areas of expertise and collaborate effectively across different disciplines. This demand has given rise to the concept of T-shaped experts who possess a deep understanding of one topic domain and a broad knowledge of several others. This combination allows these professionals to be more creative, flexible, and adaptable in problem-solving by leveraging their diverse perspectives and experiences. To find T-shaped experts in any skill area, we need to measure how deep and wide their knowledge is. In this paper, we present a novel translation-based method to estimate each user's depth of knowledge in a given skill area. The proposed method leverages a self-attention-based multi-label classification network to identify the most relevant translations for each skill that belongs to the given skill area. We utilize two new methods based on binary cross-entropy and focal loss to determine whether a user's expertise shape matches the T-shaped. We evaluate the proposed method using the standard benchmark datasets. The experimental results on three collections of the StackOverflow dataset demonstrate the superiority of the proposed methods in comparison with existing baselines.

Potential Role of Generative Adversarial Networks in Enhancing Brain Tumors.

Contrast enhancement is necessary for visualizing, diagnosing, and treating brain tumors. Through this study, we aimed to examine the potential role of general adversarial neural networks in generating artificial intelligence-based enhancement of tumors using a lightweight model. A retrospective study was conducted on magnetic resonance imaging scans of patients diagnosed with brain tumors between 2020 and 2023. A generative adversarial neural network was built to generate images that would mimic the real contrast enhancement of these tumors. The performance of the neural network was evaluated quantitatively by VGG-16, ResNet, binary cross-entropy loss, mean absolute error, mean squared error, and structural similarity index measures. Regarding the qualitative evaluation, nine cases were randomly selected from the test set and were used to build a short satisfaction survey for experienced medical professionals. One hundred twenty-nine patients with 156 scans were identified from the hospital database. The data were randomly split into a training set and validation set (90%) and a test set (10%). The VGG loss function for training, validation, and test sets were 2,049.8, 2,632.6, and 4,276.9, respectively. Additionally, the structural similarity index measured 0.366, 0.356, and 0.3192, respectively. At the time of submitting the article, 23 medical professionals responded to the survey. The median overall satisfaction score was 7 of 10. Our network would open the door for using lightweight models in performing artificial contrast enhancement. Further research is necessary in this field to reach the point of clinical practicality.

Enhancing soccer pass receiver prediction in broadcast images through advanced deep learning techniques: A comprehensive study on model optimization and performance evaluation

In this study, we present a graph neural network (GNN) model specifically designed for football pass receiver prediction in Broadcast Images is presented in this study. Important node properties, including ball possession indicators, hot-encoded team values, and normalized ground placements, are incorporated into the model along with a careful weighting of edges to account for player distances. With weighted BCE loss used to overcome class imbalance, its architecture consists of a linear layer, numerous GNN Message Passing layers, a SoftMax activation, and binary cross-entropy (BCE) loss for training. Of 206 examples, 101 valid predictions were made, indicating a predictive accuracy of 0.50 according to the evaluation data. Comparative analyzes show that GAT-V2 (0.85) and GAT (0.63) perform better in terms of optimization and accuracy, respectively. The effectiveness in recognizing football pass receivers is demonstrated in this paper, highlighting developments in computer vision applications for sports analytics.