Semi-automatic Annotation Research Articles

Deep learning contour-based method for semi-automatic annotation of manufactured objects in electron microscopy images

Deep learning contour-based method for semi-automatic annotation of manufactured objects in electron microscopy images

Automatization of CT Annotation: Combining AI Efficiency with Expert Precision.

The integration of artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) algorithms, marks a transformative progression in medical imaging diagnostics. This technical note elucidates a novel methodology for semantic segmentation of the vertebral column in CT scans, exemplified by a dataset of 250 patients from Riga East Clinical University Hospital. Our approach centers on the accurate identification and labeling of individual vertebrae, ranging from C1 to the sacrum-coccyx complex. Patient selection was meticulously conducted, ensuring demographic balance in age and sex, and excluding scans with significant vertebral abnormalities to reduce confounding variables. This strategic selection bolstered the representativeness of our sample, thereby enhancing the external validity of our findings. Our workflow streamlined the segmentation process by eliminating the need for volume stitching, aligning seamlessly with the methodology we present. By leveraging AI, we have introduced a semi-automated annotation system that enables initial data labeling even by individuals without medical expertise. This phase is complemented by thorough manual validation against established anatomical standards, significantly reducing the time traditionally required for segmentation. This dual approach not only conserves resources but also expedites project timelines. While this method significantly advances radiological data annotation, it is not devoid of challenges, such as the necessity for manual validation by anatomically skilled personnel and reliance on specialized GPU hardware. Nonetheless, our methodology represents a substantial leap forward in medical data semantic segmentation, highlighting the potential of AI-driven approaches to revolutionize clinical and research practices in radiology.

IEEG-recon: A fast and scalable pipeline for accurate reconstruction of intracranial electrodes and implantable devices.

Clinicians use intracranial electroencephalography (iEEG) in conjunction with noninvasive brain imaging to identify epileptic networks and target therapy for drug-resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms. We created iEEG-recon, a scalable electrode reconstruction pipeline for semiautomatic iEEG annotation, rapid image registration, and electrode assignment on brain magnetic resonance imaging (MRI). Its modular architecture includes a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG-recon in a containerized format that allows integration into clinical workflows. We propose a cloud-based implementation of iEEG-recon and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts. We used iEEG-recon to accurately reconstruct electrodes in both electrocorticography and stereoelectroencephalography cases with a 30-min running time per case (including semiautomatic electrode labeling and reconstruction). iEEG-recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre- and postimplant T1-MRI visual inspections. We also found that our use of ANTsPyNet deep learning-based brain segmentation for electrode classification was consistent with the widely used FreeSurfer segmentations. iEEG-recon is a robust pipeline for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting fast data analysis and integration into clinical workflows. iEEG-recon's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide.

The Construction of a Shared Resource Base for Teaching Chinese Culture under the Architecture of Disciplinary Knowledge Mapping

Abstract This paper proposes to use knowledge mapping to construct a shared resource base for Chinese culture teaching to realize the optimization and innovation of Chinese culture teaching. The internal data and external data of the Chinese teaching resource base are obtained through two ways textbook informatization and automatic semantic annotation. Then the data are preprocessed to ensure the accuracy of the data in the resource base. On this basis, the LSTM-CRF model is used to identify the entity naming of the knowledge points of Chinese teaching resources. Then, the named knowledge points of Chinese culture teaching resources are integrated and stored to complete the systematic construction of the Chinese culture teaching resource base. The empirical analysis of the proposed resource base is carried out. The data present that the average oral score of the students in the photo group is 37.2583, while that of the students in the experimental group is 43.1465, and the difference between the average scores of the two classes is about 5 points, which is a large score difference. The two-tailed significance sig value is 0.005 if equal variances are used, and the two-tailed significance sig value is 0.008 if equal variances are not used, both of which are less than 0.05, indicating that the method of this paper has a positive effect on improving the speaking scores of the students of the comprehensive Chinese culture class.

Accurate Airway Tree Segmentation in CT Scans via Anatomy-aware Multi-class Segmentation and Topology-guided Iterative Learning.

Intrathoracic airway segmentation in computed tomography is a prerequisite for various respiratory disease analyses such as chronic obstructive pulmonary disease, asthma and lung cancer. Due to the low imaging contrast and noises execrated at peripheral branches, the topological-complexity and the intra-class imbalance of airway tree, it remains challenging for deep learning-based methods to segment the complete airway tree (on extracting deeper branches). Unlike other organs with simpler shapes or topology, the airway's complex tree structure imposes an unbearable burden to generate the "ground truth" label (up to 7 or 3 hours of manual or semi-automatic annotation per case). Most of the existing airway datasets are incompletely labeled/annotated, thus limiting the completeness of computer-segmented airway. In this paper, we propose a new anatomy-aware multi-class airway segmentation method enhanced by topology-guided iterative self-learning. Based on the natural airway anatomy, we formulate a simple yet highly effective anatomy-aware multi-class segmentation task to intuitively handle the severe intra-class imbalance of the airway. To solve the incomplete labeling issue, we propose a tailored iterative self-learning scheme to segment toward the complete airway tree. For generating pseudo-labels to achieve higher sensitivity (while retaining similar specificity), we introduce a novel breakage attention map and design a topology-guided pseudo-label refinement method by iteratively connecting breaking branches commonly existed from initial pseudo-labels. Extensive experiments have been conducted on four datasets including two public challenges. The proposed method achieves the top performance in both EXACT'09 challenge using average score and ATM'22 challenge on weighted average score. In a public BAS dataset and a private lung cancer dataset, our method significantly improves previous leading approaches by extracting at least (absolute) 6.1% more detected tree length and 5.2% more tree branches, while maintaining comparable precision.

Semi-Automatic Annotation of 3D Radar and Camera for Smart Infrastructure-Based Perception

Semi-Automatic Annotation of 3D Radar and Camera for Smart Infrastructure-Based Perception

Annotation method of risk data in a certain field based on pattern matching

With the development of information technology and the increasing complexity of industrial technology, there is an urgent need for a certain field to use big data and artificial intelligence to improve the management and decision-making level. In order to classify the field’s risk text data through intelligent algorithms, analysing the risk distribution and the major problems, this paper researches on the annotation methods of training data in this field. The proposed data annotation method is based on pattern matching, addressing the special problems of risk data annotation in this field (such as strong professionalism, small data volume, high accuracy requirement and timeliness requirements). A new matching pattern is generated through the steps of text segmentation, keyword extraction, pattern preliminary generation, pattern relation tree construction, pattern optimization, pattern generalization, pattern verification, classification and annotation, and final classification and annotation are performed after pattern matching. Performance tests in terms of accuracy, recall rate, and annotation time have shown that the overall performance of the proposed method outperforms that of traditional item-by-item manual annotation, and semi-automatic annotation methods through machine learning. The method described in this paper has strong application value for risk data annotation in this field, and also has certain reference significance for high-density, high-accuracy and high-timeliness data annotation in other fields.

SACCHARIS v2: Streamlining Prediction of Carbohydrate-Active Enzyme Specificities Within Large Datasets.

Carbohydrates are chemically and structurally diverse, composed of a wide array of monosaccharides, stereochemical linkages, substituent groups, and intermolecular associations with other biological molecules. A large repertoire of carbohydrate-active enzymes (CAZymes) and enzymatic activities are required to form, dismantle, and metabolize these complex molecules. The software SACCHARIS (Sequence Analysis and Clustering of CarboHydrate Active enzymes for Rapid Informed prediction of Specificity) provides a rapid, easy-to-use pipeline for the prediction of potential CAZyme function in new datasets. We have updated SACCHARIS to (i) simplify its installation by re-writing in Python and packaging for Conda; (ii) enhance its usability through a new (optional) interactive GUI; and (iii) enable semi-automated annotation of phylogenetic tree output via a new R package or the commonly-used webserver iTOL. Significantly, SACCHARIS v2 has been developed with high-throughput omics in mind, with pipeline automation geared toward complex (meta)genome and (meta)transcriptome datasets to reveal the total CAZyme content ("CAZome") of an organism or community. Here, we outline the development and use of SACCHARIS v2 to discover and annotate CAZymes and provide insight into complex carbohydrate metabolisms in individual organisms and communities.

The Role of Temporal and Spectral Cues in Non-native Speech Production:

This study investigated the role of durational and spectral cues in second language tense and lax vowel contrasts produced by non-native speakers. To test previous claims that speakers primarily rely on durational cues over spectral cues to distinguish L2 tense and lax vowel pairs, citation style speech data were collected from 16 native speakers of Bangla; participants were all undergraduate students. The data were collected via a shadowing task where participants listened to a carefully constructed list of English words in random order and repeated each word immediately after they heard them. The utterances were recorded via a Zoom H1n voice recorder. Collected speech data were annotated and processed using the phonetic analysis software Praat and the semi-automatic annotation toolkit DARLA; statistical analyses were performed using R statistical computing software. Results indicate that Bangla speakers do not emphasize on durational cues to differentiate English tense-lax vowel pairs, contrary to the general patterns reported from other languages; rather, they prefer the spectral cues over the durational cues.

Fine-tuning coreference resolution for different styles of clinical narratives

ObjectiveCoreference resolution (CR) is a natural language processing (NLP) task that is concerned with finding all expressions within a single document that refer to the same entity. This makes it crucial in supporting downstream NLP tasks such as summarization, question answering and information extraction. Despite great progress in CR, our experiments have highlighted a substandard performance of the existing open-source CR tools in the clinical domain. We set out to explore some practical solutions to fine-tune their performance on clinical data. MethodsWe first explored the possibility of automatically producing silver standards following the success of such an approach in other clinical NLP tasks. We designed an ensemble approach that leverages multiple models to automatically annotate co-referring mentions. Subsequently, we looked into other ways of incorporating human feedback to improve the performance of an existing neural network approach. We proposed a semi-automatic annotation process to facilitate the manual annotation process. We also compared the effectiveness of active learning relative to random sampling in an effort to further reduce the cost of manual annotation. ResultsOur experiments demonstrated that the silver standard approach was ineffective in fine-tuning the CR models. Our results indicated that active learning should also be applied with caution. The semi-automatic annotation approach combined with continued training was found to be well suited for the rapid transfer of CR models under low-resource conditions. The ensemble approach demonstrated a potential to further improve accuracy by leveraging multiple fine-tuned models. ConclusionOverall, we have effectively transferred a general CR model to a clinical domain. Our findings based on extensive experimentation have been summarized into practical suggestions for rapid transferring of CR models across different styles of clinical narratives.

Chest X-Ray Image Annotation based on Spatial Relationship Feature Extraction

Digital imaging has become an essential element in every medical institution. Therefore, medical image retrieval such as chest X-ray (CXR) must be improved via novel feature extraction and annotation activities before they are stored into image databases. To date, many methods have been introduced to annotate medical images using spatial relationships after these features are extracted. However, the annotation performance for each method is inconsistent and does not show promising achievement to retrieve images. It is noticed that each method is still struggling with at least two big problems. Firstly, the recommended annotation model is weak because the method does not consider the object shape and rely on gross object shape estimation. Secondly, the suggested annotation model can only be functional for simple object placement. As a result, it is difficult to determine the spatial relationship feature after they are extracted to annotate images accurately. Hence, this study aims to propose a new model to annotate nodule location within lung zone for CXR image with extracted spatial relationship feature to improve image retrieval. In order to achieve the aim, a methodology that consists of six phases of CXR image annotation using the extracted spatial relationship features is introduced. This comprehensive methodology covers all cycles for image annotation tasks starting from image pre-processing until determination of spatial relationship features for the lung zone in the CXR. The outcome from applying the methodology also enables us to produce a new semi-automatic annotation system named CHEXRIARS which acts as a tool to annotate the extracted spatial relationship features in CXR images. The CHEXRIARS performance is tested using a retrieval test with two common tests namely the precision and recall (PNR). Apart from CHEXRIARS, three other annotation methods that are object slope, object projection and comparison of region boundaries are also included in the retrieval performance test. Overall, the CHEXRIARS interpolated PNR curve shows the best shape because it is the closest curve approaching the value of 1 on the X-axis and Y-axis. Meanwhile the value of area under curve for CHEXRIARS also revealed that this system attained the highest score at 0.856 as compared to the other three annotation methods. The outcome from the retrieval performance test indicated that the proposed annotation model has produced outstanding outcome and improved the image retrieval.

Applying Human-in-the-Loop to construct a dataset for determining content reliability to combat fake news

Annotated corpora are indispensable tools to train computational models in Natural Language Processing. However, in the case of more complex semantic annotation processes, it is a costly, arduous, and time-consuming task, resulting in a shortage of resources to train Machine Learning and Deep Learning algorithms. In consideration, this work proposes a methodology, based on the human-in-the-loop paradigm, for semi-automatic annotation of complex tasks. This methodology is applied in the construction of a reliability dataset of Spanish news so as to combat disinformation and fake news. We obtain a high quality resource by implementing the proposed methodology for semi-automatic annotation, increasing annotator efficacy and speed, with fewer examples. The methodology consists of three incremental phases and results in the construction of the RUN dataset. The annotation quality of the resource was evaluated through time-reduction (annotation time reduction of almost 64% with respect to the fully manual annotation), annotation quality (measuring consistency of annotation and inter-annotator agreement), and performance by training a model with RUN semi-automatic dataset (Accuracy 95% F1 95%), validating the suitability of the proposal.

Developing Multi-Labelled Corpus of Twitter Short Texts: A Semi-Automatic Method

Facing fast-increasing electronic documents in the Digital Media Age, the need to extract textual features of online texts for better communication is growing. Sentiment classification might be the key method to catch emotions of online communication, and developing corpora with annotation of emotions is the first step to achieving sentiment classification. However, the labour-intensive and costly manual annotation has resulted in the lack of corpora for emotional words. Furthermore, single-label semantic corpora could hardly meet the requirement of modern analysis of complicated user’s emotions, but tagging emotional words with multiple labels is even more difficult than usual. Improvement of the methods of automatic emotion tagging with multiple emotion labels to construct new semantic corpora is urgently needed. Taking Twitter short texts as the case, this study proposes a new semi-automatic method to annotate Internet short texts with multiple labels and form a multi-labelled corpus for further algorithm training. Each sentence is tagged with both the emotional tendency and polarity, and each tweet, which generally contains several sentences, is tagged with the first two major emotional tendencies. The semi-automatic multi-labelled annotation is achieved through the process of selecting the base corpus and emotional tags, data preprocessing, automatic annotation through word matching and weight calculation, and manual correction in case of multiple emotional tendencies are found. The experiments on the Sentiment140 published Twitter corpus demonstrate the effectiveness of the proposed approach and show consistency between the results of semi-automatic annotation and manual annotation. By applying this method, this study summarises the annotation specification and constructs a multi-labelled emotion corpus with 6500 tweets for further algorithm training.

In-the-Wild Affect Analysis of Children with ASD Using Heart Rate.

Recognizing the affective state of children with autism spectrum disorder (ASD) in real-world settings poses challenges due to the varying head poses, illumination levels, occlusion and a lack of datasets annotated with emotions in in-the-wild scenarios. Understanding the emotional state of children with ASD is crucial for providing personalized interventions and support. Existing methods often rely on controlled lab environments, limiting their applicability to real-world scenarios. Hence, a framework that enables the recognition of affective states in children with ASD in uncontrolled settings is needed. This paper presents a framework for recognizing the affective state of children with ASD in an in-the-wild setting using heart rate (HR) information. More specifically, an algorithm is developed that can classify a participant's emotion as positive, negative, or neutral by analyzing the heart rate signal acquired from a smartwatch. The heart rate data are obtained in real time using a smartwatch application while the child learns to code a robot and interacts with an avatar. The avatar assists the child in developing communication skills and programming the robot. In this paper, we also present a semi-automated annotation technique based on facial expression recognition for the heart rate data. The HR signal is analyzed to extract features that capture the emotional state of the child. Additionally, in this paper, the performance of a raw HR-signal-based emotion classification algorithm is compared with a classification approach based on features extracted from HR signals using discrete wavelet transform (DWT). The experimental results demonstrate that the proposed method achieves comparable performance to state-of-the-art HR-based emotion recognition techniques, despite being conducted in an uncontrolled setting rather than a controlled lab environment. The framework presented in this paper contributes to the real-world affect analysis of children with ASD using HR information. By enabling emotion recognition in uncontrolled settings, this approach has the potential to improve the monitoring and understanding of the emotional well-being of children with ASD in their daily lives.

A Multi-Scale Rebar Detection Network with an Embedded Attention Mechanism

With the rapid development of computer vision and machine vision, methods based on deep learning have achieved good results in the field of object detection, identification, and tracking. However, for the detection and identification of rebars in smart construction sites, it is very difficult to perform accurate real-time detection of rebars by using object detection technology on the equipment in the field because of the problems of the dense cross-section between bundled bars, the mutual adhesion of cross-section boundaries, and mutual occlusion between cross-sections. To address the above problems, we propose a multi-scale rebar detection network RebarNet with an embedded attention mechanism based on YOLOv5, combining the K-means++ algorithm, attention mechanism, a newly designed SD_IoU Loss, and multi-scale feature fusion, aiming to solve the problems of missed and false detection in dense small object detection. Due to the problems of scarce rebar cross-section datasets, no publicly available large datasets, and weak rebar cross-sectional features, we constructed a new rebar cross-sectional dataset, used a semi-automatic annotation method to annotate part of the dataset, and then used the data enhancement algorithm to expand the rebar dataset. The experimental results show that the average accuracy (mAP) of our proposed RebarNet network is 97.9%, which is comparable to mainstream target detection algorithms such as Faster R-CNN, SSD, RetinaNet, CenterNet, CornerNet, YOLOv3, YOLOv4, and YOLOv5s. mAP0.5 is improved by 8.1%, 13%, 26.4%, 25.8%, 26.2%, 11.7%, 7.6%, and 9%, respectively. In addition, the frames per second (FPS) transmission reaches 89 frames per second, the model weight is only 17.0 MB. In summary, the proposed RebarNet can effectively reduce missed and false detections in the rebar counting detection task based on real-time detection.

Semi-automatic Annotation for Mentions in Hindi Text

Semi-automatic Annotation for Mentions in Hindi Text

Semantic automatic annotation method based on artificial intelligence for electric power internet of things

AbstractThe development of the power Internet of Things is currently underway, and a proposal for a semantic Internet of Things based on artificial intelligence algorithms is made to address the challenges in obtaining prior knowledge for heterogeneous data fusion, improving the real‐time performance of the ontology library, and enhancing the efficiency of manual labeling of instance object data in the power field. This proposal introduces an Automatic Semantic Annotation Method to provide an effective knowledge organization model for sensor systems. Data mining knowledge is utilized to drive ontology update and improvement, resulting in more accurate semantic annotation and enhanced machine understanding. Experimental results show that artificial intelligence algorithms can automatically extract concepts from sensory data and achieve automatic semantic annotation during ontology instantiation.

Machine learning based quantitative characterization of microstructures

Identifying structure–property relationships of polycrystalline microstructures demands an accurate and precise quantification of their features. Measuring grain sizes is tedious and creates a non-transparent bias when being performed by different individuals, impairing comparability with references. Here, we present a novel use of region-based convolutional neural networks (R-CNNs) to quantify several microstructural characteristics and their distributions: Feret diameter, axis length, area, circumference, dihedral angle and coordination number. We utilize a two-step approach: (i) a semi-automatic annotation tool to generate training data for (ii) a fully automated R-CNN, quantitatively evaluating images. Using Al-doped ZnO as a model system, we trained two R-CNNs, one for ZnO and one for precipitated ZnAl2O4. The R-CNN performs well in evaluating grain size characteristics from images with low contrast and in differentiating uni-, and bimodal grain size distributions on the sub-micron, and nanoscale. An extended statistical analysis of the distributions is performed to extract microstructural parameters quantitatively. This innovative solution makes grain size measuring amenable, time-effective, less biased, consistent, and statistically more precise.

A semi-automatic annotation methodology that combines Summarization and Human-In-The-Loop to create disinformation detection resources

Early detection of disinformation is one of the most challenging big-scale problems facing present day society. This is why the application of technologies such as Artificial Intelligence and Natural Language Processing is necessary. The vast majority of Artificial Intelligence approaches require annotated data, and generating these resources is very expensive. This proposal aims to improve the efficiency of the annotation process with a two-level semi-automatic annotation methodology. The first level extracts relevant information through summarization techniques. The second applies a Human-in-the-Loop strategy whereby the labels are pre-annotated by the machine, corrected by the human and reused by the machine to retrain the automatic annotator. After evaluating the system, the average annotation time per news item is reduced by 50%. In addition, a set of experiments on the semi-automatically annotated dataset that is generated are performed so as to demonstrate the effectiveness of the proposal. Although the dataset is annotated in terms of unreliable content, it is applied to the veracity detection task with very promising results (0.95 accuracy in reliability detection and 0.78 in veracity detection).

Semi-automatic classification of digital heritage on the Aïoli open source 2D/3D annotation platform via machine learning and deep learning

Today, digital tools represent a privileged means of describing and understanding of cultural heritage. However, existing hybrid methods for semantic annotation on digital models are mostly manual and require considerable human intervention. This may cause data dispersion or data alteration issues, preventing the digital collection of multidisciplinary heritage information in collaborative valorization and conservation projects. Tackling this problem, this work presents three different methods for more automated 2D/3D data annotation, making the most of Machine Learning or Deep Learning and of the collaborative, web-based annotation platform Aïoli. The proposed approaches are designed to assist heritage experts in the hybrid annotation of architectural objects, e.g., to identify building components, degradation patterns, mapping of alterations, materials etc. and to share and access 2D/3D information via the web. The work aims at providing public and private stakeholders in charge of restoration and conservation activities with a set of more automated annotation tools, allowing to: i) preserve data in the transition to different representation types; ii) propagate analytically relevant information via digital models and/or images; iii) share heritage-related data on the web with an open source strategy. The validated workflows sketch the outlines of a dedicated and user-friendly toolbar for semi-automatic annotation, directly integrated into the Aïoli user interface, where automated annotation tools are readily available to the community of heritage professionals to properly support documentation and restoration activities.