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  1. 5161
    “…METHODS: A deep residual UNet was developed and evaluated for automated, volume-level pneumothorax grading (i.e., labelling a volume whether a pneumothorax was present or not), and pixel-level classification (i.e., segmentation and quantification of pneumothorax), on a retrospective series of routine chest CT data. Ground truth annotations were provided by radiologists. …”
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    Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography
  2. 5162
    “…The annotations of a consensus panel consisting of two expert radiologists determined the ground truth. The detection performance of the re-trained CAD model and three other radiologists at different levels of experience were tested using a free-response receiver operating characteristic (FROC) analysis in the test group. …”
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    Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network
  3. 5163
    “…This imperfect ground truth introduces a systematic error, but the effects on classifier performance are currently unknown. …”
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    Effects of Label Noise on Deep Learning-Based Skin Cancer Classification
  4. 5164
    por Wu, Di, Cai, Zhen, Han, Jiwan, Qin, Huawei
    Publicado 2020
    “…The results showed good agreement (> 93%) in terms of accuracy and precision between ground truth (manual counting) and the image-based counting. …”
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    Automatic kernel counting on maize ear using RGB images
  5. 5165
    “…We find close agreement between predicted labels using our method and the ground truth labelled by human experts. We observe the limitation of current static image classification scheme for EGD image classification. …”
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    Deep learning-based anatomical site classification for upper gastrointestinal endoscopy
  6. 5166
    por Lewin, Keith M.
    Publicado 2020
    “…The challenge is to strengthen the mechanisms to separate evidence from opinion and to balance popularism with speaking truth to power—especially when political systems can find it difficult to distinguish fact from convenient fiction. …”
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    Contingent reflections on coronavirus and priorities for educational planning and development
  7. 5167
    “…Power often derives from deep structural inequalities, but also seeps into norms and beliefs, into what we ‘know’ as truth, and what we believe about the world and about ourselves within it. …”
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    When accountability meets power: realizing sexual and reproductive health and rights
  8. 5168
    “…Both BB and conventional TFE imaging allowed for the correct detection of all contrast-enhancing tumor residuals intraoperatively (considering postsurgical MRI and histopathological evaluation as the ground truth for determination of the lack/presence of contrast-enhancing tumor residuals), but BB imaging showed significantly higher diagnostic confidence (R1: 4.65 ± 0.53 vs. 3.88 ± 1.02, p < 0.0001; R2: 4.75 ± 0.50 vs. 4.25 ± 0.81, p < 0.0001). …”
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    Assessment of the Extent of Resection in Surgery of High-Grade Glioma—Evaluation of Black Blood Sequences for Intraoperative Magnetic Resonance Imaging at 3 Tesla
  9. 5169
    “…The performance of the DLA was evaluated using the dice similarity score (DSS) for segmentation and Bland-Altman 95% limits of agreement (LOA) for measurement of the volumetric indices, which was compared with that of ground truth manual segmentation. RESULTS: In test dataset-1, the DLA achieved a mean DSS of 0.973 and 0.974 for liver and spleen segmentation, respectively, with no significant difference in DSS across different liver conditions (p = 0.60 and 0.26 for the liver and spleen, respectively). …”
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    Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images
  10. 5170
    “…Photoacoustic-based visual servoing was implemented as the fiber was manually displaced by a translation stage, which provided ground truth measurements of the fiber displacement. GPU-SLSC results were compared with a central processing unit (CPU)-SLSC approach and an amplitude-based delay-and-sum (DAS) beamforming approach. …”
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    GPU implementation of photoacoustic short-lag spatial coherence imaging for improved image-guided interventions
  11. 5171
    “…This study employed deep-learning convolutional neural networks to stage lung disease severity of Coronavirus Disease 2019 (COVID-19) infection on portable chest x-ray (CXR) with radiologist score of disease severity as ground truth. This study consisted of 131 portable CXR from 84 COVID-19 patients (51M 55.1±14.9yo; 29F 60.1±14.3yo; 4 missing information). …”
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    Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs
  12. 5172
    “…Group-level results obtained by both methods were evaluated in terms of two outcome measures: (1) inter-subject variability of response magnitude and (2) sensitivity/specificity analysis of response topography, taking as ground truth previously reported maps of the language system based on intraoperative cortical mapping as well as meta-analytic maps of language task fMRI responses. …”
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    Mapping language function with task-based vs. resting-state functional MRI
  13. 5173
    por Ramteke, Shobhana, Sahu, Bharat Lal
    Publicado 2020
    “…In late December 2019, the world woke to a truth of a pandemic of Coronavirus Disease (COVID-19), inspired by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which has a place with a gathering of beta-coronavirus. …”
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    Novel coronavirus disease 2019 (COVID-19) pandemic: Considerations for the biomedical waste sector in India
  14. 5174
    por Fortelny, Nikolaus, Bock, Christoph
    Publicado 2020
    “…We validate KPNNs on simulated data with known ground truth and demonstrate their practical use and utility in five biological applications with single-cell RNA-seq data for cancer and immune cells. …”
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    Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data
  15. 5175
    “…Accuracy was assessed by mean error (ME) with respect to ground truth conductivity. On average, ME < 0.1 Sm(−1) for all tissues. …”
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    Deep learning‐based reconstruction of in vivo pelvis conductivity with a 3D patch‐based convolutional neural network trained on simulated MR data
  16. 5176
    “…Ischemic regions, regional ischemic core and final infarction were determined on simultaneously acquired CTP and follow-up imaging as ground truth. Receiver operating characteristics analysis was performed to calculate the area-under-the-curve (AUC). …”
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    Cerebral attenuation on single-phase CT angiography source images: Automated ischemia detection and morphologic outcome prediction after thrombectomy in patients with ischemic stroke
  17. 5177
    “…ImmunoGlobe provides a ground truth immune interaction network upon which such tools can be built. …”
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    ImmunoGlobe: enabling systems immunology with a manually curated intercellular immune interaction network
  18. 5178
    “…For machine learning algorithms, we used the patient diagnosis as the ground truth label and patient findings as machine learning features. …”
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    Evaluation of standard and semantically-augmented distance metrics for neurology patients
  19. 5179
    “…METHOD: Deep convolutional neural network algorithms for lung segmentation and opacity quantification on chest CTs were trained using semi-automatically and manually created ground-truth (N(total) = 172). The performance of the in-house method was compared to an externally developed algorithm on a separate test subset (N = 66). …”
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    Development and clinical implementation of tailored image analysis tools for COVID-19 in the midst of the pandemic: The synergetic effect of an open, clinically embedded software development platform and machine learning
  20. 5180
    “…Purpose: Our study predictively maps epithelium density in magnetic resonance imaging (MRI) space while varying the ground truth labels provided by five pathologists to quantify the downstream effects of interobserver variability. …”
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    Radio-pathomic mapping model generated using annotations from five pathologists reliably distinguishes high-grade prostate cancer
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