ADKU/ResearchGPT_model_dataset huggingface dataset:
Automated classification of polyps using deep learning architectures and few-shot learning
Authors: Adrian Krenzer, Stefan Heil, Daniel Fitting, Safa Matti, Wolfram G. Zoller, Alexander Hann, Frank Puppe

BackgroundColorectal cancer is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is a colonoscopy. However, not all colon polyps have the risk of becoming cancerous. Therefore, polyps are classified using different classification systems. After the classification, further treatment and procedures are based on the classification of the polyp. Nevertheless, classification is not easy. Therefore, we suggest two novel automated classifications system assisting gastroenterologists in classifying polyps based on the NICE and Paris classification.MethodsWe build two classification systems. One is classifying polyps based on their shape (Paris). The other classifies polyps based on their texture and surface patterns (NICE). A two-step process for the Paris classification is introduced: First, detecting and cropping the polyp on the image, and secondly, classifying the polyp based on the cropped area with a transformer network. For the NICE classification, we design a few-shot learning algorithm based on the Deep Metric Learning approach. The algorithm creates an embedding space for polyps, which allows classification from a few examples to account for the data scarcity of NICE annotated images in our database.ResultsFor the Paris classification, we achieve an accuracy of 89.35 %, surpassing all papers in the literature and establishing a new state-of-the-art and baseline accuracy for other publications on a public data set. For the NICE classification, we achieve a competitive accuracy of 81.13 % and demonstrate thereby the viability of the few-shot learning paradigm in polyp classification in data-scarce environments. Additionally, we show different ablations of the algorithms. Finally, we further elaborate on the explainability of the system by showing heat maps of the neural network explaining neural activations.ConclusionOverall we introduce two polyp classification systems to assist gastroenterologists. We achieve state-of-the-art performance in the Paris classification and demonstrate the viability of the few-shot learning paradigm in the NICE classification, addressing the prevalent data scarcity issues faced in medical machine learning.
Deep learning prediction of survival in patients with heart failure using chest radiographs
Authors: Han Jia, Shengen Liao, Xiaomei Zhu, Wangyan Liu, Yi Xu, Rongjun Ge, Yinsu Zhu

Heart failure (HF) is associated with high rates of morbidity and mortality. The value of deep learning survival prediction models using chest radiographs in patients with heart failure is currently unclear. The aim of our study is to develop and validate a deep learning survival prediction model using chest X-ray (DLSPCXR) in patients with HF. The study retrospectively enrolled a cohort of 353 patients with HF who underwent chest X-ray (CXR) at our institution between March 2012 and March 2017. The dataset was randomly divided into training (n = 247) and validation (n = 106) datasets. Univariate and multivariate Cox analysis were conducted on the training dataset to develop clinical and imaging survival prediction models. The DLSPCXR was trained and the selected clinical parameters were incorporated into DLSPCXR to establish a new model called DLSPinteg. Discrimination performance was evaluated using the time-dependent area under the receiver operating characteristic curves (TD AUC) at 1, 3, and 5-years survival. Delong’s test was employed for the comparison of differences between two AUCs of different models. The risk-discrimination capability of the optimal model was evaluated by the Kaplan–Meier curve. In multivariable Cox analysis, older age, higher N-terminal pro-B-type natriuretic peptide (NT-ProBNP), systolic pulmonary artery pressure (sPAP) > 50 mmHg, New York Heart Association (NYHA) functional class III–IV and cardiothoracic ratio (CTR) ≥ 0.62 in CXR were independent predictors of poor prognosis in patients with HF. Based on the receiver operating characteristic (ROC) curve analysis, DLSPCXR had better performance at predicting 5-year survival than the imaging Cox model in the validation cohort (AUC: 0.757 vs. 0.561, P = 0.01). DLSPinteg as the optimal model outperforms the clinical Cox model (AUC: 0.826 vs. 0.633, P = 0.03), imaging Cox model (AUC: 0.826 vs. 0.555, P < 0.001), and DLSPCXR (AUC: 0.826 vs. 0.767, P = 0.06). Deep learning models using chest radiographs can predict survival in patients with heart failure with acceptable accuracy.
Leveraging distant supervision and deep learning for twitter sentiment and emotion classification
Authors: Muhamet Kastrati, Zenun Kastrati, Ali Shariq Imran, Marenglen Biba

Nowadays, various applications across industries, healthcare, and security have begun adopting automatic sentiment analysis and emotion detection in short texts, such as posts from social media. Twitter stands out as one of the most popular online social media platforms due to its easy, unique, and advanced accessibility using the API. On the other hand, supervised learning is the most widely used paradigm for tasks involving sentiment polarity and fine-grained emotion detection in short and informal texts, such as Twitter posts. However, supervised learning models are data-hungry and heavily reliant on abundant labeled data, which remains a challenge. This study aims to address this challenge by creating a large-scale real-world dataset of 17.5 million tweets. A distant supervision approach relying on emojis available in tweets is applied to label tweets corresponding to Ekman’s six basic emotions. Additionally, we conducted a series of experiments using various conventional machine learning models and deep learning, including transformer-based models, on our dataset to establish baseline results. The experimental results and an extensive ablation analysis on the dataset showed that BiLSTM with FastText and an attention mechanism outperforms other models in both classification tasks, achieving an F1-score of 70.92% for sentiment classification and 54.85% for emotion detection.
Advances in Deep Learning Models for Resolving Medical Image Segmentation Data Scarcity Problem: A Topical Review
Authors: Ashwini Kumar Upadhyay, Ashish Kumar Bhandari

Deep learning (DL) methods have recently become state-of-the-art in most automated medical image segmentation tasks. Some of the biggest challenges in this field are related to datasets. This paper aims to review the recent developments in deep learning architectures and approaches that aim to resolve dataset-related challenges faced in DL-based medical image segmentation. We have studied architectural developments in deep learning models and their recent applications in medical image segmentation tasks. Popular U-Net-based models are tested for segmentation performance comparison on a Coronavirus disease 2019 (Covid-19) lung infection Computed Tomography segmentation dataset. The comparison results prove the effectiveness of the original U-Net architecture, even in present-day medical image segmentation tasks. To overcome major dataset-related challenges such as labeled data scarcity, high annotation time and cost, distribution shifts, low-quality of images, and generalizability issues; we have studied recent developments in deep learning approaches like active learning, data augmentation, domain adaptation, and self- and semi-supervised learning, that aim to provide innovative solutions for those challenges. With rapid developments in the field, approaches like data augmentation, domain adaptation, and semi-supervised learning have become some of the hot areas of research, aiming for more efficient use of datasets, better segmentation prediction, and model generalizability.
An efficient intrusive deep reinforcement learning framework for OpenFOAM
Authors: Saeed Salehi

Recent advancements in artificial intelligence and deep learning offer tremendous opportunities to tackle high-dimensional and challenging problems. Particularly, deep reinforcement learning (DRL) has been shown to be able to address optimal decision-making problems and control complex dynamical systems. DRL has received increased attention in the realm of computational fluid dynamics (CFD) due to its demonstrated ability to optimize complex flow control strategies. However, DRL algorithms often suffer from low sampling efficiency and require numerous interactions between the agent and the environment, necessitating frequent data exchanges. One significant bottleneck in coupled DRL–CFD algorithms is the extensive data communication between DRL and CFD codes. Non-intrusive algorithms where the DRL agent treats the CFD environment as a black box may come with the deficiency of increased computational cost due to overhead associated with the information exchange between the two DRL and CFD modules. In this article, a TensorFlow-based intrusive DRL–CFD framework is introduced where the agent model is integrated within the open-source CFD solver OpenFOAM. The integration eliminates the need for any external information exchange during DRL episodes. The framework is parallelized using the message passing interface to manage parallel environments for computationally intensive CFD cases through distributed computing. The performance and effectiveness of the framework are verified by controlling the vortex shedding behind two and three-dimensional cylinders, achieved as a result of minimizing drag and lift forces through an active flow control mechanism. The simulation results indicate that the trained controller can stabilize the flow and effectively mitigate the vortex shedding.
Heart failure classification using deep learning to extract spatiotemporal features from ECG
Authors: Chang-Jiang Zhang, Yuan-Lu, Fu-Qin Tang, Hai-Peng Cai, Yin-Fen Qian, Chao-Wang

BackgroundHeart failure is a syndrome with complex clinical manifestations. Due to increasing population aging, heart failure has become a major medical problem worldwide. In this study, we used the MIMIC-III public database to extract the temporal and spatial characteristics of electrocardiogram (ECG) signals from patients with heart failure.MethodsWe developed a NYHA functional classification model for heart failure based on a deep learning method. We introduced an integrating attention mechanism based on the CNN-LSTM-SE model, segmenting the ECG signal into 2 to 20 s long segments. Ablation experiments showed that the 12 s ECG signal segments could be used with the proposed deep learning model for superior classification of heart failure.ResultsThe accuracy, positive predictive value, sensitivity, and specificity of the NYHA functional classification method were 99.09, 98.9855, 99.033, and 99.649%, respectively.ConclusionsThe comprehensive performance of this model exceeds similar methods and can be used to assist in clinical medical diagnoses.
Drug target prediction through deep learning functional representation of gene signatures
Authors: Hao Chen, Frederick J. King, Bin Zhou, Yu Wang, Carter J. Canedy, Joel Hayashi, Yang Zhong, Max W. Chang, Lars Pache, Julian L. Wong, Yong Jia, John Joslin, Tao Jiang, Christopher Benner, Sumit K. Chanda, Yingyao Zhou

Many machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of OMICS datasets, including target deconvolution and mechanism of action studies, we develop an approach that represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We develop the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model and demonstrate that its application to the Broad Institute’s L1000 datasets results in more effective compound-target predictions than models based on gene identities alone. By integrating additional pharmacological activity data sources, FRoGS significantly increases the number of high-quality compound-target predictions relative to existing approaches, many of which are supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help uncover new relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts.
Deep learning-based automated steel surface defect segmentation: a comparative experimental study
Authors: Dejene M. Sime, Guotai Wang, Zhi Zeng, Bei Peng

The use of machine vision and deep learning for intelligent industrial inspection has become increasingly important in automating the production processes. Despite the fact that machine vision approaches are used for industrial inspection, deep learning-based defect segmentation has not been widely studied. While state-of-the-art segmentation methods are often tuned for a specific purpose, extending them to unknown sets or other datasets, such as defect segmentation datasets, require further analysis. In addition, recent contributions and improvements in image segmentation methods have not been extensively investigated for defect segmentation. To address these problems, we conducted a comparative experimental study on several recent state-of-the-art deep learning-based segmentation methods for steel surface defect segmentation and evaluated them on the basis of segmentation performance, processing time, and computational complexity using two public datasets, NEU-Seg and Severstal Steel Defect Detection (SSDD). In addition we proposed and trained a hybrid transformer-based encoder with CNN-based decoder head and achieved state-of-the-art results, a Dice score of 95.22% (NEU-Seg) and 95.55% (SSDD).
Metasurface-empowered snapshot hyperspectral imaging with convex/deep (CODE) small-data learning theory
Authors: Chia-Hsiang Lin, Shih-Hsiu Huang, Ting-Hsuan Lin, Pin Chieh Wu

Hyperspectral imaging is vital for material identification but traditional systems are bulky, hindering the development of compact systems. While previous metasurfaces address volume issues, the requirements of complicated fabrication processes and significant footprint still limit their applications. This work reports a compact snapshot hyperspectral imager by incorporating the meta-optics with a small-data convex/deep (CODE) deep learning theory. Our snapshot hyperspectral imager comprises only one single multi-wavelength metasurface chip working in the visible window (500-650 nm), significantly reducing the device area. To demonstrate the high performance of our hyperspectral imager, a 4-band multispectral imaging dataset is used as the input. Through the CODE-driven imaging system, it efficiently generates an 18-band hyperspectral data cube with high fidelity using only 18 training data points. We expect the elegant integration of multi-resonant metasurfaces with small-data learning theory will enable low-profile advanced instruments for fundamental science studies and real-world applications.
Performance evaluation of deep learning approaches for predicting mechanical fields in composites
Authors: Marwa Yacouti, Maryam Shakiba

This paper presents a rigorous and critical methodology for evaluating the performance of deep learning (DL) techniques in predicting mechanical responses within the microstructural representation of composites. In the past few years, deep learning has emerged as a powerful tool and an efficient surrogate for finite element analysis in computational mechanics. This research addresses questions regarding the suitability of common error metrics for evaluating the accuracy of DL techniques in predicting full-field mechanical responses of composites. Through comparative analysis, we evaluate the performance and identify the limitations of two DL frameworks in predicting the linear von Mises stress distribution within the microstructure of the selected fiber-reinforced composite. The first DL method is based on the residual network, while the second utilizes U-Net architecture. We use several evaluation metrics, including different types of error and statistical measures and examine their suitability. Additionally, this study also investigates the influence of the size of the training and validation dataset, ranging from 50 to 2000 samples, on the predictive performance of the employed ResNet and U-Net based approaches.
Application of Deep Learning in the Classification of Maritime Safety Information
Authors: Pengbo Sun, Yi Zuo, Xinyu Li, Yudi Wang

Maritime safety information (MSI) refers to urgent information concerning navigation warnings, weather warnings, weather forecasts, and other safety-related information for navigation. MSI is primarily disseminated through two systems: the international NAVTEX (Navigational Telex) system and the Enhanced Group Calling (EGC) system. NAVTEX receivers on ships can automatically receive MSI, enhancing safety of life and property at sea and reducing the workload for communication personnel. Due to the narrowband direct printing telegraphy (NBDP) technology used in the broadcast of MSI, the information must be concise, it makes the information difficult for navigators to interpret. To address this challenge, various machine learning solutions have been proposed. Among these, deep neural networks have shown superior performance in MSI classification. This study provides a more detailed analysis of deep neural networks applied to MSI classification. It utilizes collected MSI datasets, including thousands of navigation warnings, and compares the performances of different models based on accuracy, precision, recall, and F1-score.
Integrated deep learning with explainable artificial intelligence for enhanced landslide management
Authors: Saeed Alqadhi, Javed Mallick, Meshel Alkahtani

Landslides pose significant threats to mountainous regions, causing widespread damage to both property and human lives. This study seeks to enhance landslide prediction in the Aqabat Al-Sulbat Asir region of Saudi Arabia by integrating deep neural networks (DNNs), 1D convolutional neural networks (CNNs), and a combined DNN and CNN ensemble (DCN) with explainable artificial intelligence (XAI) techniques. These XAI techniques enhance the interpretability of these complex deep learning models, thereby facilitating better decision-making strategies. Furthermore, the DNN model is employed to incorporate game theory principles, assessing the individual impact of variables on landslide prediction. Our findings indicate high and very high landslide susceptibility zones covering 35.1–41.32 km2 and 15.14–16.2 km2, respectively. The DCN model boasts the highest area under the curve (AUC) at 0.97, followed by CNN (0.94) and DNN (0.9), showcasing DCN's superiority. XAI analysis exposes significant residuals in CNN's posterior despite its high AUC. Notably, precipitation, slope, soil texture, and line density emerge as pivotal parameters for accurate landslide prediction. Game theory results highlight line density's preeminence, trailed by topographic wetness index, curvature, and slope in landslide occurrence. By incorporating deep learning models, XAI, and game theory, this study presents a holistic approach to landslide management. This comprehensive framework equips authorities and stakeholders with valuable tools for informed decision-making in landslide-prone areas, delivering accurate predictions and insights into crucial parameters.
Deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram for predicting Ki-67 expression in rectal cancer
Authors: Sikai Wu, Neng Wang, Weiqun Ao, Jinwen Hu, Wenjie Xu, Guoqun Mao

PurposeTo explore the value of deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram in predicting the Ki-67 expression in rectal cancer.MethodsThe data of 491 patients with rectal cancer from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. They were categorized into high- and low-expression group based on postoperative pathological Ki-67 expression. Each patient’s mp-MRI data were analyzed to extract and select the most relevant features of deep learning, and a deep learning model was constructed. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a nomogram for the prediction of Ki-67 expression. The performance characteristics of the DL-model, clinical model, and nomogram were assessed using ROCs, calibration curve, decision curve, and clinical impact curve analysis.ResultsThe strongest deep learning features were extracted and screened from mp-MRI data. Two independent predictive factors, namely Magnetic Resonance Imaging T (mrT) staging and differentiation degree, were identified through clinical feature selection. Three models were constructed: a deep learning (DL)-model, a clinical model, and a nomogram. The AUCs of clinical model in the training, internal validation, and external validation set were 0.69, 0.78, and 0.67, respectively. The AUCs of the deep model and nomogram ranged from 0.88 to 0.98. The prediction performance of the deep learning model and nomogram was significantly better than the clinical model (P < 0.001).ConclusionThe nomogram based on deep learning can help clinicians accurately and conveniently predict the expression status of Ki-67 in rectal cancer.Graphical abstract
Fast high-frequency porosity characterization from computer tomography images and deep learning
Authors: Manuel R. V. Avila, Julio de C. V. Fernandes, Carlos E. M. dos Anjos, Adna G. P. Vasconcelos, Igor R. Cartucho, Felipe B. F. Guimaraes, Rodrigo Surmas, Alexandre G. Evsukoff

This work presents a methodology for combining deep learning with whole-core computed tomography (CT) imaging and laboratory data for fast and high-frequency estimation of porosity logs. The proposed method trains a convolutional neural network (CNN) model to predict laboratory-scale porosities by using core-scale CT images as input. Despite the different scales, the model works well to predict the porosities measured at laboratory scales using the textures present in core-scale CT images. The main contribution of this work is the data-driven methodology for fast and high-frequency characterization of porosity at the core scale. The proposed methodology can provide a much more efficient porosity characterization than the traditional workflow based on the qualitative and subjective evaluation of experts. The method was evaluated in a well-based cross-validation process in 26 wells from 3 different Brazilian pre-salt fields, using two distinct CNN architectures of varying complexity. The results show that the models can make good predictions for new wells with acceptable margins of error. The proposed method can be easily integrated into digital rock workflows as it is based on data available for traditional petrophysical analyses and deep learning models available in open source.