ADKU/ResearchGPT_model_dataset huggingface dataset:
Deep Learning Approaches for Automatic Quality Assurance of Magnetic Resonance Images Using ACR Phantom
Authors: Tarraf Torfeh, Souha Aouadi, SA Yoganathan, Satheesh Paloor, Rabih Hammoud, Noora Al-Hammadi

BackgroundIn recent years, there has been a growing trend towards utilizing Artificial Intelligence (AI) and machine learning techniques in medical imaging, including for the purpose of automating quality assurance. In this research, we aimed to develop and evaluate various deep learning-based approaches for automatic quality assurance of Magnetic Resonance (MR) images using the American College of Radiology (ACR) standards.MethodsThe study involved the development, optimization, and testing of custom convolutional neural network (CNN) models. Additionally, popular pre-trained models such as VGG16, VGG19, ResNet50, InceptionV3, EfficientNetB0, and EfficientNetB5 were trained and tested. The use of pre-trained models, particularly those trained on the ImageNet dataset, for transfer learning was also explored. Two-class classification models were employed for assessing spatial resolution and geometric distortion, while an approach classifying the image into 10 classes representing the number of visible spokes was used for the low contrast.ResultsOur results showed that deep learning-based methods can be effectively used for MR image quality assurance and can improve the performance of these models. The low contrast test was one of the most challenging tests within the ACR phantom.ConclusionsOverall, for geometric distortion and spatial resolution, all of the deep learning models tested produced prediction accuracy of 80% or higher. The study also revealed that training the models from scratch performed slightly better compared to transfer learning. For the low contrast, our investigation emphasized the adaptability and potential of deep learning models. The custom CNN models excelled in predicting the number of visible spokes, achieving commendable accuracy, recall, precision, and F1 scores.
Depression clinical detection model based on social media: a federated deep learning approach
Authors: Yang Liu

Depression can significantly impact people’s mental health, and recent research shows that social media can provide decision-making support for health-care professionals and serve as supplementary information for understanding patients’ health status. Deep learning models are also able to assess an individual’s likelihood of experiencing depression. However, data availability on social media is often limited due to privacy concerns, even though deep learning models benefit from having more data to analyze. To address this issue, this study proposes a methodological framework system for clinical decision support that uses federated deep learning (FDL) to identify individuals experiencing depression and provide intervention decisions for clinicians. The proposed framework involves evaluation of datasets from three social media platforms, and the experimental results demonstrate that our method achieves state-of-the-art results. The study aims to provide a clinical decision support system with evolvable features that can deliver precise solutions and assist health-care professionals in medical diagnosis. The proposed framework that incorporates social media data and deep learning models can provide valuable insights into patients’ health status, support personalized treatment decisions, and adapt to changing health-care needs.
Detecting schizophrenia with 3D structural brain MRI using deep learning
Authors: Junhao Zhang, Vishwanatha M. Rao, Ye Tian, Yanting Yang, Nicolas Acosta, Zihan Wan, Pin-Yu Lee, Chloe Zhang, Lawrence S. Kegeles, Scott A. Small, Jia Guo

Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis using a single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3D whole-brain structure using standard post-processing methods. A deep learning model was then developed, optimized, and evaluated on three open datasets with T1-weighted MRI scans of patients with schizophrenia. Our proposed model outperformed the benchmark model, which was also trained with structural MR images using a 3D CNN architecture. Our model is capable of almost perfectly (area under the ROC curve = 0.987) distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Regional analysis localized subcortical regions and ventricles as the most predictive brain regions. Subcortical structures serve a pivotal role in cognitive, affective, and social functions in humans, and structural abnormalities of these regions have been associated with schizophrenia. Our finding corroborates that schizophrenia is associated with widespread alterations in subcortical brain structure and the subcortical structural information provides prominent features in diagnostic classification. Together, these results further demonstrate the potential of deep learning to improve schizophrenia diagnosis and identify its structural neuroimaging signatures from a single, standard T1-weighted brain MRI.
Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning
Authors: Jaakko Sahlsten, Joel Jaskari, Kareem A. Wahid, Sara Ahmed, Enrico Glerean, Renjie He, Benjamin H. Kann, Antti Mäkitie, Clifton D. Fuller, Mohamed A. Naser, Kimmo Kaski

BackgroundRadiotherapy is a core treatment modality for oropharyngeal cancer (OPC), where the primary gross tumor volume (GTVp) is manually segmented with high interobserver variability. This calls for reliable and trustworthy automated tools in clinician workflow. Therefore, accurate uncertainty quantification and its downstream utilization is critical.MethodsHere we propose uncertainty-aware deep learning for OPC GTVp segmentation, and illustrate the utility of uncertainty in multiple applications. We examine two Bayesian deep learning (BDL) models and eight uncertainty measures, and utilize a large multi-institute dataset of 292 PET/CT scans to systematically analyze our approach.ResultsWe show that our uncertainty-based approach accurately predicts the quality of the deep learning segmentation in 86.6% of cases, identifies low performance cases for semi-automated correction, and visualizes regions of the scans where the segmentations likely fail.ConclusionsOur BDL-based analysis provides a first-step towards more widespread implementation of uncertainty quantification in OPC GTVp segmentation.
Insights from EEG analysis of evoked memory recalls using deep learning for emotion charting
Authors: Muhammad Najam Dar, Muhammad Usman Akram, Ahmad Rauf Subhani, Sajid Gul Khawaja, Constantino Carlos Reyes-Aldasoro, Sarah Gul

Affect recognition in a real-world, less constrained environment is the principal prerequisite of the industrial-level usefulness of this technology. Monitoring the psychological profile using smart, wearable electroencephalogram (EEG) sensors during daily activities without external stimuli, such as memory-induced emotions, is a challenging research gap in emotion recognition. This paper proposed a deep learning framework for improved memory-induced emotion recognition leveraging a combination of 1D-CNN and LSTM as feature extractors integrated with an Extreme Learning Machine (ELM) classifier. The proposed deep learning architecture, combined with the EEG preprocessing, such as the removal of the average baseline signal from each sample and extraction of EEG rhythms (delta, theta, alpha, beta, and gamma), aims to capture repetitive and continuous patterns for memory-induced emotion recognition, underexplored with deep learning techniques. This work has analyzed EEG signals using a wearable, ultra-mobile sports cap while recalling autobiographical emotional memories evoked by affect-denoting words, with self-annotation on the scale of valence and arousal. With extensive experimentation using the same dataset, the proposed framework empirically outperforms existing techniques for the emerging area of memory-induced emotion recognition with an accuracy of 65.6%. The EEG rhythms analysis, such as delta, theta, alpha, beta, and gamma, achieved 65.5%, 52.1%, 65.1%, 64.6%, and 65.0% accuracies for classification with four quadrants of valence and arousal. These results underscore the significant advancement achieved by our proposed method for the real-world environment of memory-induced emotion recognition.
A Resource-Efficient Deep Learning Approach to Visual-Based Cattle Geographic Origin Prediction
Authors: Camellia Ray, Sambit Bakshi, Pankaj Kumar Sa, Ganapati Panda

Customized healthcare for cattle health monitoring is essential, which aims to optimize individual animal health, thereby enhancing productivity, minimizing illness-related risks, and improving overall welfare. Tailoring healthcare practices to individual requirements guarantees that individual animals receive proper attention and intervention, resulting in better health outcomes and sustainable cattle farming practices. In this regard, the manuscript proposes a visual cues-based region prediction methodology to design a customized cattle healthcare system. The proposed automated AI healthcare system uses resource-efficient deep learning-inspired architecture for computer vision applications like performing region-wise classification. The classification mechanism can be used further to identify a cattle and the regions it belongs. Extensive experimentation has been conducted on a redesigned image dataset to identify the best-suited deep-learning framework to perform region classification for livestock, such as cattle. MobileNetV2 outperforms the considered state-of-the-art frameworks by achieving an accuracy of 93% in identifying the regions of the cattle.
Hybrid deep learning framework for weather forecast with rainfall prediction using weather bigdata analytics
Authors: C. Lalitha, D. Ravindran

The volume and complexity of weather data, along with missing values and high correlation between collected variables, make it challenging to develop efficient deep learning frameworks that can handle data with more features. This leads to a lack of accurate and predictable weather forecasts. To develop a hybrid deep learning framework for weather forecast with rainfall prediction using weather big data analytics to ensure high detection rates. A modified planet optimization (MPO) algorithm is used for data preprocessing to remove unwanted artifacts. An improved Tuna optimization (ITO) algorithm is presented to select optimal features to avoid data dimensionality issues. A hybrid memory-augmented artificial neural network (MA-ANN) classifier is developed to improve weather early forecast detection rates. The proposed framework is validated against standard benchmark datasets such as weather underground and climate forecast system reanalysis (CFSR). The simulation results are compared with other existing state-of-the-art frameworks based on error measures (RMSE, MAPE, BIAS, R) and quality measures (accuracy, sensitivity, specificity, precision, F1-measure).The MA-ANN classifier accuracy obtained 97.65% for wunderground.com Delhi and 98.88% for Tamilnadu. The hybrid deep learning framework with rainfall prediction using weather big data analytics has shown promising results for accurate and predictable weather forecasts. The proposed framework outperforms other existing state-of-the-art frameworks, and the MA-ANN classifier has improved weather early forecast detection rates. The study demonstrates the potential of utilizing big data techniques in weather forecasting and highlights the importance of developing efficient deep learning frameworks to handle complex and high-dimensional weather data.
DCLGM: Fusion Recommendation Model Based on LightGBM and Deep Learning
Authors: Bin Zhao, Bin Li, Jiqun Zhang, Wei Cao, Yilong Gao

The recommendation system can mine valuable information according to user preferences, so it is widely used in various industries. However, the performance of recommendation systems is generally affected by the problem of data sparsity, and LightGBM can alleviate the impact caused by data sparsity to a certain extent. To this end, this paper proposes a fusion recommendation model based on the LightGBM and deep learning—CLGM model. The model is composed of LighGBM, cross network and deep neural network. First, the features in the dataset are fused and extracted through LightGBM, and the feature with the highest classification accuracy is selected as the input of the neural network layer; Then, using the cross network and the deep neural network, the linear cross combination feature relationship and nonlinear correlation relationship between high-order features are respectively obtained; finally, the results obtained by the pre-order network are linearly weighted and combined to obtain the final recommendation result. In this paper, AUC and Logloss are used as evaluation indicators to verify the model on the public dataset Criteo and dataset Avazu. The simulation experiment results show that, compared with the four typical recommendation models, the recommendation effect of this model is better.
Automated assembly quality inspection by deep learning with 2D and 3D synthetic CAD data
Authors: Xiaomeng Zhu, Pär Mårtensson, Lars Hanson, Mårten Björkman, Atsuto Maki

In the manufacturing industry, automatic quality inspections can lead to improved product quality and productivity. Deep learning-based computer vision technologies, with their superior performance in many applications, can be a possible solution for automatic quality inspections. However, collecting a large amount of annotated training data for deep learning is expensive and time-consuming, especially for processes involving various products and human activities such as assembly. To address this challenge, we propose a method for automated assembly quality inspection using synthetic data generated from computer-aided design (CAD) models. The method involves two steps: automatic data generation and model implementation. In the first step, we generate synthetic data in two formats: two-dimensional (2D) images and three-dimensional (3D) point clouds. In the second step, we apply different state-of-the-art deep learning approaches to the data for quality inspection, including unsupervised domain adaptation, i.e., a method of adapting models across different data distributions, and transfer learning, which transfers knowledge between related tasks. We evaluate the methods in a case study of pedal car front-wheel assembly quality inspection to identify the possible optimal approach for assembly quality inspection. Our results show that the method using Transfer Learning on 2D synthetic images achieves superior performance compared with others. Specifically, it attained 95% accuracy through fine-tuning with only five annotated real images per class. With promising results, our method may be suggested for other similar quality inspection use cases. By utilizing synthetic CAD data, our method reduces the need for manual data collection and annotation. Furthermore, our method performs well on test data with different backgrounds, making it suitable for different manufacturing environments.
Domain knowledge enhanced deep learning for electrocardiogram arrhythmia classification
Authors: Jie Sun 
            (孙洁)

Deep learning provides an effective way for automatic classification of cardiac arrhythmias, but in clinical decision-making, pure data-driven methods working as black-boxes may lead to unsatisfactory results. A promising solution is combining domain knowledge with deep learning. This paper develops a flexible and extensible framework for integrating domain knowledge with a deep neural network. The model consists of a deep neural network to capture the statistical pattern between input data and the ground-truth label, and a knowledge module to guarantee consistency with the domain knowledge. These two components are trained interactively to bring the best of both worlds. The experiments show that the domain knowledge is valuable in refining the neural network prediction and thus improves accuracy.
Development of Deep Learning Color Recognition Model for Color Measurement Processes
Authors: Sanghun Lee, Ki-Sub Kim, Jeong Won Kang

We present a deep learning color recognition model for the color measurement process in the paint industry. Currently, spectrophotometers are primarily used for color measurements owing to their accuracy. The measurement method involves manually injecting the sample into a spectrophotometer. Our proposed method uses a webcam with a deep learning model on the stand of a spectrophotometer. Deep learning models are widely used for image and color detection. In this study, the “you only look once (YOLO)” algorithm is applied for real-time detection of color samples. Upon training various sample images using YOLO, the model could detect the sample area in real time using a webcam. An open source computer vision (OpenCV) library was used for the color recognition model, and the detected RGB color value was converted to the international commission on illumination color space (CIELAB) value, which is primarily used in the color measuring process. However, because of the mirror-like reflection of light from a surface with specular reflection, it is difficult to implement the color value using a camera. To address this problem, we compare several specular removal methods and propose the most suitable model for the color recognition model of color samples. The accuracy of the proposed model was verified by comparing the colors of various samples. Our proposed approach can easily detect samples and color values, which can contribute significantly to automatically calculating the exact amount of coloring required for the target color.
Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients
Authors: Jingjing Zhang, Qiang Zhang, Bo Zhao, Gaofeng Shi

PurposeDeveloped and validated a deep learning radiomics nomogram using multi-phase contrast-enhanced computed tomography (CECT) images to predict neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) patients.MethodsThis multi-center study retrospectively included 322 patients diagnosed with gastric cancer from January 2013 to June 2023 at two hospitals. Handcrafted radiomics technique and the EfficientNet V2 neural network were applied to arterial, portal venous, and delayed phase CT images to extract two-dimensional handcrafted and deep learning features. A nomogram model was built by integrating the handcrafted signature, the deep learning signature, with clinical features. Discriminative ability was assessed using the receiver operating characteristics (ROC) curve and the precision-recall (P-R) curve. Model fitting was evaluated using calibration curves, and clinical utility was assessed through decision curve analysis (DCA).ResultsThe nomogram exhibited excellent performance. The area under the ROC curve (AUC) was 0.848 [95% confidence interval (CI), 0.793–0.893)], 0.802 (95% CI 0.688–0.889), and 0.751 (95% CI 0.652–0.833) for the training, internal validation, and external validation sets, respectively. The AUCs of the P-R curves were 0.838 (95% CI 0.756–0.895), 0.541 (95% CI 0.329–0.740), and 0.556 (95% CI 0.376–0.722) for the corresponding sets. The nomogram outperformed the clinical model and handcrafted signature across all sets (all P < 0.05). The nomogram model demonstrated good calibration and provided greater net benefit within the relevant threshold range compared to other models.ConclusionThis study created a deep learning nomogram using CECT images and clinical data to predict NAC response in LAGC patients undergoing surgical resection, offering personalized treatment insights.Graphical abstract
Towards a universal mechanism for successful deep learning
Authors: Yuval Meir, Yarden Tzach, Shiri Hodassman, Ofek Tevet, Ido Kanter

Recently, the underlying mechanism for successful deep learning (DL) was presented based on a quantitative method that measures the quality of a single filter in each layer of a DL model, particularly VGG-16 trained on CIFAR-10. This method exemplifies that each filter identifies small clusters of possible output labels, with additional noise selected as labels outside the clusters. This feature is progressively sharpened with each layer, resulting in an enhanced signal-to-noise ratio (SNR), which leads to an increase in the accuracy of the DL network. In this study, this mechanism is verified for VGG-16 and EfficientNet-B0 trained on the CIFAR-100 and ImageNet datasets, and the main results are as follows. First, the accuracy and SNR progressively increase with the layers. Second, for a given deep architecture, the maximal error rate increases approximately linearly with the number of output labels. Third, similar trends were obtained for dataset labels in the range [3, 1000], thus supporting the universality of this mechanism. Understanding the performance of a single filter and its dominating features paves the way to highly dilute the deep architecture without affecting its overall accuracy, and this can be achieved by applying the filter’s cluster connections (AFCC).
Fast reconstruction of EEG signal compression sensing based on deep learning
Authors: XiuLi Du, KuanYang Liang, YaNa Lv, ShaoMing Qiu

When traditional EEG signals are collected based on the Nyquist theorem, long-time recordings of EEG signals will produce a large amount of data. At the same time, limited bandwidth, end-to-end delay, and memory space will bring great pressure on the effective transmission of data. The birth of compressed sensing alleviates this transmission pressure. However, using an iterative compressed sensing reconstruction algorithm for EEG signal reconstruction faces complex calculation problems and slow data processing speed, limiting the application of compressed sensing in EEG signal rapid monitoring systems. As such, this paper presents a non-iterative and fast algorithm for reconstructing EEG signals using compressed sensing and deep learning techniques. This algorithm uses the improved residual network model, extracts the feature information of the EEG signal by one-dimensional dilated convolution, directly learns the nonlinear mapping relationship between the measured value and the original signal, and can quickly and accurately reconstruct the EEG signal. The method proposed in this paper has been verified by simulation on the open BCI contest dataset. Overall, it is proved that the proposed method has higher reconstruction accuracy and faster reconstruction speed than the traditional CS reconstruction algorithm and the existing deep learning reconstruction algorithm. In addition, it can realize the rapid reconstruction of EEG signals.