Application Of Deep Learning Research Articles

Deep Learning and Multimodal Artificial Intelligence in Orthopaedic Surgery.

This review article focuses on the applications of deep learning with neural networks and multimodal neural networks in the orthopaedic domain. By providing practical examples of how artificial intelligence (AI) is being applied successfully in orthopaedic surgery, particularly in the realm of imaging data sets and the integration of clinical data, this study aims to provide orthopaedic surgeons with the necessary tools to not only evaluate existing literature but also to consider AI's potential in their own clinical or research pursuits. We first review standard deep neural networks which can analyze numerical clinical variables, then describe convolutional neural networks which can analyze image data, and then introduce multimodal AI models which analyze various types of different data. Then, we contrast these deep learning techniques with related but more limited techniques such as radiomics, describe how to interpret deep learning studies, and how to initiate such studies at your institution. Ultimately, by empowering orthopaedic surgeons with the knowledge and know-how of deep learning, this review aspires to facilitate the translation of research into clinical practice, thereby enhancing the efficacy and precision of real-world orthopaedic care for patients.

Predicting oil palm yield using a comprehensive agronomy dataset and 17 machine learning and deep learning models

The rising global demand for oil palm emphasizes the importance of accurate oil palm yield predictions. This predictive capability is critical for effective crop management, supply chain optimization, and sustainable farming practices. However, the oil palm sector faces challenges in yield projection, stressing a noteworthy gap in the application and evaluation of modern machine learning and deep learning technologies. Our study addressed this gap by systematically evaluating 17 machine and deep learning models in predicting oil palm yield, incorporating various agronomic parameters, e.g., soil composition, climatic conditions, plant age, and farming techniques. This holistic approach enhances the application of machine and deep learning in agriculture. Using the feature selection technique and a maximum depth of 32 and 1000 estimators, the Extra Trees Regressor exhibited positive performance, i.e., MSE = 860.36 and an R2 = 0.65, and stands out among the 17 models evaluated. Our findings also showed that incorporating a comprehensive agronomic dataset is critical to accurate yield prediction. Hence, this model and approach have the potential to be a robust decision-making tool for agronomists and farmers in the oil palm industry, setting the stage for future innovations in sustainable agriculture practices.

Random vector functional link networks for function approximation on manifolds

The learning speed of feed-forward neural networks is notoriously slow and has presented a bottleneck in deep learning applications for several decades. For instance, gradient-based learning algorithms, which are used extensively to train neural networks, tend to work slowly when all of the network parameters must be iteratively tuned. To counter this, both researchers and practitioners have tried introducing randomness to reduce the learning requirement. Based on the original construction of Igelnik and Pao, single layer neural-networks with random input-to-hidden layer weights and biases have seen success in practice, but the necessary theoretical justification is lacking. In this study, we begin to fill this theoretical gap. We then extend this result to the non-asymptotic setting using a concentration inequality for Monte-Carlo integral approximations. We provide a (corrected) rigorous proof that the Igelnik and Pao construction is a universal approximator for continuous functions on compact domains, with approximation error squared decaying asymptotically like O(1/n) for the number n of network nodes. We then extend this result to the non-asymptotic setting, proving that one can achieve any desired approximation error with high probability provided n is sufficiently large. We further adapt this randomized neural network architecture to approximate functions on smooth, compact submanifolds of Euclidean space, providing theoretical guarantees in both the asymptotic and non-asymptotic forms. Finally, we illustrate our results on manifolds with numerical experiments.

Application and difficulties of deep learning in drug target discovery

Application and difficulties of deep learning in drug target discovery

Research on the Application of Deep Learning in Medical Image Segmentation and 3D Reconstruction

Medical image segmentation (MIS) and 3D reconstruction are crucial research directions in the field of medical imaging, which is of great significance for disease diagnosis, treatment planning and surgical navigation. In recent years, with the rapid development of Deep Learning (DL) technology, DL has made remarkable progress in the field of medical image processing and has become one of the important methods of MIS and 3D reconstruction. In this paper, the application of DL technologies in MIS and 3D reconstruction is systematically studied and discussed. Firstly, the paper introduces the basic concepts and technical challenges of MIS and 3D reconstruction, including image quality, noise interference and edge detection. Secondly, the paper introduces the data acquisition process in detail, including the medical image data set and data preprocessing method. Then, the paper puts forward the DL model framework based on self-attention mechanism, as well as the loss function and optimizer used in the training process. Then, the model is verified by experiments, and the performance of different models in MIS and 3D reconstruction is analyzed. Finally, the experimental results are comprehensively analyzed, and the application prospect and future development direction of DL in MIS and 3D reconstruction are discussed. The research results of this paper provide important theoretical and practical guidance for improving medical image processing technology and promoting the development and clinical application of medical imaging.

Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.

Breast cancer is the most prevalent cancer among women, and its diagnosis requires the accurate identification and classification of histological features for effective patient management. Artificial intelligence, particularly through deep learning, represents the next frontier in cancer diagnosis and management. Notably, the use of convolutional neural networks and emerging Vision Transformers (ViT) has been reported to automate pathologists' tasks, including tumor detection and classification, in addition to improving the efficiency of pathology services. Deep learning applications have also been extended to the prediction of protein expression, molecular subtype, mutation status, therapeutic efficacy, and outcome prediction directly from hematoxylin and eosin-stained slides, bypassing the need for immunohistochemistry or genetic testing. This review explores the current status and prospects of deep learning in breast cancer diagnosis with a focus on whole-slide image analysis. Artificial intelligence applications are increasingly applied to many tasks in breast pathology ranging from disease diagnosis to outcome prediction, thus serving as valuable tools for assisting pathologists and supporting breast cancer management.

Boundary-Aware Abstractive Summarization with Entity-Augmented Attention for Enhancing Faithfulness

With the successful application of deep learning, document summarization systems can produce more readable results. However, abstractive summarization still suffers from unfaithful outputs and factual errors, especially in named entities. Current approaches tend to employ external knowledge to improve model performance while neglecting the boundary information and the semantics of the entities. In this article, we propose an entity-augmented method (EAM) to encourage the model to make full use of the entity boundary information and pay more attention to the critical entities. Experimental results on three Chinese and English summarization datasets show that our method outperforms several strong baselines and achieves state-of-the-art performance on the CLTS dataset. Our method can also improve the faithfulness of the summary and generalize well to different pre-trained language models. Moreover, we propose a method to evaluate the integrity of generated entities. Besides, we adapt the data augmentation method in the FactCC model according to the difference between Chinese and English in grammar and train a new evaluation model for factual consistency evaluation in Chinese summarization.

Pure Vision Transformer (CT-ViT) with Noise2Neighbors Interpolation for Low-Dose CT Image Denoising.

Convolutional neural networks (CNN) have been used for a wide variety of deep learning applications, especially in computer vision. For medical image processing, researchers have identified certain challenges associated with CNNs. These challenges encompass the generation of less informative features, limitations in capturing both high and low-frequency information within feature maps, and the computational cost incurred when enhancing receptive fields by deepening the network. Transformers have emerged as an approach aiming to address and overcome these specific limitations of CNNs in the context of medical image analysis. Preservation of all spatial details of medical images is necessary to ensure accurate patient diagnosis. Hence, this research introduced the use of a pure Vision Transformer (ViT) for a denoising artificial neural network for medical image processing specifically for low-dose computed tomography (LDCT) image denoising. The proposed model follows a U-Net framework that contains ViT modules with the integration of Noise2Neighbor (N2N) interpolation operation. Five different datasets containing LDCT and normal-dose CT (NDCT) image pairs were used to carry out this experiment. To test the efficacy of the proposed model, this experiment includes comparisons between the quantitative and visual results among CNN-based (BM3D, RED-CNN, DRL-E-MP), hybrid CNN-ViT-based (TED-Net), and the proposed pure ViT-based denoising model. The findings of this study showed that there is about 15-20% increase in SSIM and PSNR when using self-attention transformers than using the typical pure CNN. Visual results also showed improvements especially when it comes to showing fine structural details of CT images.

A memristive all-inclusive hypernetwork for parallel analog deployment of full search space architectures

In recent years, there has been a significant advancement in memristor-based neural networks, positioning them as a pivotal processing-in-memory deployment architecture for a wide array of deep learning applications. Within this realm of progress, the emerging parallel analog memristive platforms are prominent for their ability to generate multiple feature maps in a single processing cycle. However, a notable limitation is that they are specifically tailored for neural networks with fixed structures. As an orthogonal direction, recent research reveals that neural architecture should be specialized for tasks and deployment platforms. Building upon this, the neural architecture search (NAS) methods effectively explore promising architectures in a large design space. However, these NAS-based architectures are generally heterogeneous and diversified, making it challenging for deployment on current single-prototype, customized, parallel analog memristive hardware circuits. Therefore, investigating memristive analog deployment that overrides the full search space is a promising and challenging problem. Inspired by this, and beginning with the DARTS search space, we study the memristive hardware design of primitive operations and propose the memristive all-inclusive hypernetwork that covers 2×1025 network architectures. Our computational simulation results on 3 representative architectures (DARTS-V1, DARTS-V2, PDARTS) show that our memristive all-inclusive hypernetwork achieves promising results on the CIFAR10 dataset (89.2% of PDARTS with 8-bit quantization precision), and is compatible with all architectures in the DARTS full-space. The hardware performance simulation indicates that the memristive all-inclusive hypernetwork costs slightly more resource consumption (nearly the same in power, 22%∼25% increase in Latency, 1.5× in Area) relative to the individual deployment, which is reasonable and may reach a tolerable trade-off deployment scheme for industrial scenarios.

CNN based plant disease identification using PYNQ FPGA

This research presents a novel approach for plant disease identification utilizing Convolutional Neural Networks (CNNs) and the PYNQ FPGA platform. The study leverages the parallel processing capabilities of FPGAs to accelerate CNN inference, aiming to enhance the efficiency of plant disease detection in agricultural settings. The implementation involves optimizing the CNN architecture for deployment on the PYNQ FPGA, considering factors such as image size and learning rates. Through experimentation, the research refines hyper parameters, achieving improved accuracy and F1 scores. Visualizations using heat maps highlight the CNN's reliance on color, shape, and texture for feature extraction in disease identification. The integration of FPGA technology demonstrates promising advancements in real-time, high-performance plant disease classification, offering potential benefits for precision agriculture and crop management. This research contributes to the growing field of FPGA-accelerated deep learning applications in agro technology, addressing challenges in plant health monitoring and fostering sustainable agricultural practices.

Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules

The simplified molecular-input line-entry system (SMILES) has been utilized in a variety of artificial intelligence analyses owing to its capability of representing chemical structures using line notation. However, its ease of representation is limited, which has led to the proposal of BigSMILES as an alternative method suitable for the representation of macromolecules. Nevertheless, research on BigSMILES remains limited due to its preprocessing requirements. Thus, this study proposes a conversion workflow of BigSMILES, focusing on its automated generation from SMILES representations of homopolymers. BigSMILES representations for 4,927,181 records are provided, thereby enabling its immediate use for various research and development applications. Our study presents detailed descriptions on a validation process to ensure the accuracy, interchangeability, and robustness of the conversion. Additionally, a systematic overview of utilized codes and functions that emphasizes their relevance in the context of BigSMILES generation are produced. This advancement is anticipated to significantly aid researchers and facilitate further studies in BigSMILES representation, including potential applications in deep learning and further extension to complex structures such as copolymers.

One-shot skill assessment in high-stakes domains with limited data via meta learning

Deep Learning (DL) has achieved robust competency assessment in various high-stakes fields. However, the applicability of DL models is often hampered by their substantial data requirements and confinement to specific training domains. This prevents them from transitioning to new tasks where data is scarce. Therefore, domain adaptation emerges as a critical element for the practical implementation of DL in real-world scenarios. Herein, we introduce A-VBANet, a novel meta-learning model capable of delivering domain-agnostic skill assessment via one-shot learning. Our methodology has been tested by assessing surgical skills on five laparoscopic and robotic simulators and real-life laparoscopic cholecystectomy. Our model successfully adapted with accuracies up to 99.5 % in one-shot and 99.9 % in few-shot settings for simulated tasks and 89.7 % for laparoscopic cholecystectomy. This study marks the first instance of a domain-agnostic methodology for skill assessment in critical fields setting a precedent for the broad application of DL across diverse real-life domains with limited data.

ECR 2024 Abstract Highlights

The following highlights spotlight selected abstracts presented at the European Congress of Radiology (ECR) 2024. Covering a range of topics, from deep learning applications in neurological disorders to using a single cranial CT slice for identifying deceased individuals, these highlights present the latest cutting-edge developments in radiology.

How Satellite Imaging and Deep Learning are Influenced by Tensor Decompositions: A Review

This study focuses on examining various approaches for Tensor decomposition (TDs) and their potential applications in satellite imaging (SI) and deep learning (DL). The research highlights how these decompositions can contribute to the advancement of SI and DL, providing valuable insights for future utilization of TDs in research. It explores how these techniques contribute to the advancement of SI and DL methods, providing insights into their potential applications and suggesting future research directions. The study aims to enhance the use of TDs techniques to further advance research efforts in these fields. More importantly, the current investigation offers a thorough analysis of the potential advantages and reasons for employing TDs techniques in different domains, including satellite imaging and deep learning. The aim is to enhance research outcomes by utilizing TDs. The review also identifies unresolved issues and proposes future directions for further investigation in this field. Fundamentally, these proposed open problems will open new grounds to the research community to articulate, innovate, and provide more real-life applications to improve the current state of the art by delving into a wider vision for a higher-level performance of both satellite imaging (SI) and deep learning (DL). Looking at the bigger scenario, this also suggests that TDs could be potentially employed to revolutionize existing machine learning technologies as well as the current space AI industry

Research and Application of Deep Learning in Medical Image Reconstruction and Enhancement

In recent years, deep learning technology has made remarkable progress in medical image reconstruction and enhancement, and has become one of the research hotspots in the field of medical image processing. This paper discusses the latest research progress and application of deep learning in medical image reconstruction and enhancement. Firstly, the importance of medical image reconstruction and enhancement and the limitations of traditional methods are introduced. Then, a detailed discussion was conducted on the application of deep learning models, including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Autoencoders, in medical image processing. Specifically, an analysis and comparison were conducted on the image reconstruction ability of CNN models, the image enhancement effect of GAN models, and the image denoising and reconstruction of Autoencoder models. Then, the advantages and challenges of deep learning model in medical image processing are discussed, and the future development direction is discussed. Finally, the research results of this paper are summarized and the prospect of future research is put forward. The research in this paper provides some enlightenment and reference for researchers and practitioners in the field of medical image processing, which is helpful to promote the continuous innovation and progress of medical image processing technology.

Application of Image Segmentation Algorithms in Computer Vision

In the field of computer vision (CV), image segmentation technology, as a fundamental part, has a crucial impact on the accuracy of subsequent image processing tasks. Image segmentation is not only a crucial transitional step from image processing to image analysis, but also a hot and difficult research topic in the field of CV. Although significant progress has been made in the research of image segmentation algorithms, existing segmentation algorithms may still face challenges in certain specific scenarios due to the complexity and diversity of images, making it difficult to achieve ideal segmentation results. In recent years, the rapid development of deep learning (DL) technology has brought new breakthroughs to the field of image segmentation. DL models, especially Convolutional Neural Networks (CNNs), can capture semantic information of images more accurately by automatically learning feature representations in images, thereby achieving more precise image segmentation. This article delves into the research and application of image segmentation algorithms in CV, with a focus on the application of DL in the field of image segmentation. With the continuous development of advanced technologies such as DL, it is believed that image segmentation technology will play a greater role in more fields in the future.

Complexity Analysis of Chinese Text Based on the Construction Grammar Theory and Deep Learning

Due to the complexity of Chinese and the differences between Chinese and English, the application of Chinese text in the digital field has a certain complexity. Taking Chinese text in Open Relation Extraction (ORE) as the research object, the complexity of Chinese text is analyzed. An extraction system of word vectors based on construction grammar theory and Deep Learning (DL) is constructed to achieve smooth extraction of Chinese text. The work of this paper mainly includes the following aspects. To study the application of DL in the complexity analysis of Chinese text based on construction grammar, firstly, the connotation of construction grammar and its role in Chinese text analysis are explored. Secondly, from the perspective of the ORE of word vectors in language analysis, an ORE model based on word vectors is implemented. Moreover, an extraction method based on the distance of word vectors is proposed. The test results show that the F1 value of the proposed algorithm is 67% on the public WEB-500 and NYT-500 datasets, which is superior to other similar text extraction algorithms. When the recall rate is more than 30%, the accuracy of the proposed method is higher than several other latest language analysis systems. This indicates that the proposed Chinese text extraction system based on the DL algorithm and construction grammar theory has advantages in complexity analysis and can provide a new research idea for Chinese text analysis.

EmoAsst: emotion recognition assistant via text-guided transfer learning on pre-trained visual and acoustic models

Children diagnosed with Autism Spectrum Disorder (ASD) often struggle to grasp social conventions and promptly recognize others' emotions. Recent advancements in the application of deep learning (DL) to emotion recognition are solidifying the role of AI-powered assistive technology in supporting autistic children. However, the cost of collecting and annotating large-scale high-quality human emotion data and the phenomenon of unbalanced performance on different modalities of data challenge DL-based emotion recognition. In response to these challenges, this paper explores transfer learning, wherein large pre-trained models like Contrastive Language-Image Pre-training (CLIP) and wav2vec 2.0 are fine-tuned to improve audio- and video-based emotion recognition with text- based guidance. In this work, we propose the EmoAsst framework, which includes a visual fusion module and emotion prompt fine-tuning for CLIP, in addition to leveraging CLIP's text encoder and supervised contrastive learning for audio-based emotion recognition on the wav2vec 2.0 model. In addition, a joint few-shot emotion classifier enhances the accuracy and offers great adaptability for real-world applications. The evaluation results on the MELD dataset highlight the outstanding performance of our methods, surpassing the majority of existing video and audio-based approaches. Notably, our research demonstrates the promising potential of the proposed text-based guidance techniques for improving video and audio-based Emotion Recognition and Classification (ERC).

Circuit fault diagnosis for 12-pulse power converter in HL-3 based on Meta Pseudo Labels

The application of deep learning in the field of power electronics fault diagnosis has garnered considerable attention and research interest. In the coil power supply of the HL-3 tokamak device, there exist practical challenges in dealing with fault data from 12-pulse thyristor power converters, including large data volume, variable frequency operation, and sensor noise, among others. To solve these problems, this paper proposes a method that combines wavelet threshold denoising and Meta Pseudo Labels. Firstly, discrete wavelet transform is used to transform the original voltage signals of the 12-pulse thyristor rectifier operating across a wide frequency range into the wavelet domain. Subsequently, a reasonable threshold is employed to filter out high-frequency noise, resulting in the output of wavelet threshold denoising. Then, Meta Pseudo Labels algorithm, an idiographic semi-supervised learning approach, is applied to learn the features of the denoised data, yielding classification models and experimental results. The model identifies three types of states in single thyristor branches of the rectifier circuit, which are normal, short-circuit fault, and open-circuit fault. The experimental results indicate that the accuracy and robustness of the model trained using the Meta Pseudo Labels algorithm outperform those of a convolutional neural network with the same network architecture. Especially, one of the experiments accomplishes a diagnosis accuracy exceeding 90% while training with the whole unlabeled training dataset and only 10% of stratified subsamples from the labeled training dataset.

Efficient High-Resolution Deep Learning: A Survey

Cameras in modern devices such as smartphones, satellites and medical equipment are capable of capturing very high resolution images and videos. Such high-resolution data often need to be processed by deep learning models for cancer detection, automated road navigation, weather prediction, surveillance, optimizing agricultural processes and many other applications. Using high-resolution images and videos as direct inputs for deep learning models creates many challenges due to their high number of parameters, computation cost, inference latency and GPU memory consumption. Simple approaches such as resizing the images to a lower resolution are common in the literature, however, they typically significantly decrease accuracy. Several works in the literature propose better alternatives in order to deal with the challenges of high-resolution data and improve accuracy and speed while complying with hardware limitations and time restrictions. This survey describes such efficient high-resolution deep learning methods, summarizes real-world applications of high-resolution deep learning, and provides comprehensive information about available high-resolution datasets.