Mostrando 121 - 140 Resultados de 168 Para Buscar '"image annotation"', tiempo de consulta: 0.17s Limitar resultados
  1. 121
    “…Two evaluation metrics, namely, tooth disease and NPDT classifications, were examined using 610 compounded and 5251 tooth images annotated by an experienced dentist with a Doctor of Dental Surgery and another dentist with a Doctor of Dental Medicine. …”
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    A smart home dental care system: integration of deep learning, image sensors, and mobile controller
  2. 122
    “…Moreover, as the existing databases of images annotated with subjective scores are using a standard gamut, two new HDR databases using WCG are proposed. …”
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    Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
  3. 123
    “…The application of the model is demonstrated with a use case spanning several steps of a real-world research pipeline — starting with the acquisition of a specimen, its processing and storage, histological examination, and the generation/collection of associated data (images, annotations, clinical data), ending with training an AI model for the detection of tumor in the images. …”
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    Lightweight Distributed Provenance Model for Complex Real–world Environments
  4. 124
    “…We present a dataset for landfill detection featuring airborne, WorldView-3, and GoogleEarth images annotated by professional photo interpreters. It comprises 3,478 positive and 6,956 negative examples. …”
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    AerialWaste dataset for landfill discovery in aerial and satellite images
  5. 125
    “…Deep learning has yielded promising results for medical image diagnosis but relies heavily on manual image annotations, which are expensive to acquire. We present Cross-DL, a cross-modality learning framework for intracranial abnormality detection and localization in head computed tomography (CT) scans by learning from free-text imaging reports. …”
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    Automatic intracranial abnormality detection and localization in head CT scans by learning from free-text reports
  6. 126
    “…The three data sets together contain 18,222 images annotated with 105,705 NLB lesions, making this the largest publicly available image set annotated for a single plant disease.…”
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    Image set for deep learning: field images of maize annotated with disease symptoms
  7. 127
    “…We provide example files for 14 Penguin Watch cameras, generated from 63,070 raw images annotated by 50,445 volunteers. We encourage the use of this large open-source dataset, and the associated processing methodologies, for both ecological studies and continued machine learning and computer vision development.…”
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    Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics
  8. 128
    “…However, these approaches typically require a large amount of ground truth training data in the form of image annotations made by human experts. As such annotation work is a very time-consuming task, there is a great need for tools that can assist in this process, saving time while not sacrificing annotation quality. …”
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    TissueWand, a Rapid Histopathology Annotation Tool
  9. 129
    “…It allows a range of static (e.g. such as shapes and colors) and dynamic (e.g. pop-up text and images) annotations. Annotated trees can be saved on the server for subsequent modification or they may be shared as IFrame HTML objects, easily embeddable in any web page. …”
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    Interactive web-based visualization and sharing of phylogenetic trees using phylogeny.IO
  10. 130
    “…This method can be applied to other datasets and requires only a minimal number of image annotations (∼50) to train this partially supervised machine-learning approach. …”
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    Estimating Conformational Traits in Dairy Cattle With DeepAPS: A Two-Step Deep Learning Automated Phenotyping and Segmentation Approach
  11. 131
    “…A total of 107 STAS-positive lung adenocarcinomas were selected and matched to 105 STAS-negative lung adenocarcinomas. Thin-slice CT imaging annotation and region of interest (ROI) segmentation were performed with semi-automatic in-house software. …”
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    Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma
  12. 132
    “…We validated the method on three datasets (Neuroblastoma, NucleusSegData, and ISBI 2009 Dataset) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional and structural point of view, guarantees the achievement of optimized and functionally correct results. …”
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    NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation
  13. 133
    “…However, WSIs present a number of unique challenges for analysis, requiring special consideration of image annotations, slide and image artefacts, and evaluation of WSI-trained model performance. …”
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    SliDL: A toolbox for processing whole-slide images in deep learning
  14. 134
    “…BACKGROUND: With the emergence of deep-learning methods, tools are needed to capture and standardize image annotations made by experimentalists. In developmental biology, cell lineages are generally reconstructed from time-lapse data. …”
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    TreeJ: an ImageJ plugin for interactive cell lineage reconstruction from static images
  15. 135
    “…METHODS: A convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. …”
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    Validation of automated artificial intelligence segmentation of optical coherence tomography images
  16. 136
    “…The method consists of three main steps, using a clarity function to filter out high-quality cervical cell images, annotating a small amount of them, and balancing the training data using a voting mechanism. …”
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    A Semi-supervised Deep Learning Method for Cervical Cell Classification
  17. 137
    “…The dataset has images annotated from RSNA-MICCAI brain tumor radio genomic competition dataset using the make sense an AI online tool for labeling dataset. …”
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    Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach
  18. 138
    “…Computational methods can enhance diagnostic yield (DY); however, incorporating machine learning algorithms (MLAs) into CE reading is difficult as large amounts of image annotations are required for training. Current databases lack graphic annotations of pathologies and cannot be used. …”
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    KID Project: an internet-based digital video atlas of capsule endoscopy for research purposes
  19. 139
    “…For this purpose, we used 10,193 images annotated with the landmarks coordinates as the ground truth. …”
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    2-step deep learning model for landmarks localization in spine radiographs
  20. 140
    “…The workgroup adopted a consensus definition of IMR as “interactive medical documentation that combines clinical images, videos, sound, imaging metadata, and/or image annotations with text, typographic emphases, tables, graphs, event timelines, anatomic maps, hyperlinks, and/or educational resources to optimize communication between medical professionals, and between medical professionals and their patients.” …”
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    Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: HIMSS-SIIM Collaborative White Paper
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