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  1. 5821
    “…As an artificial data set, D2 provided a known ground truth and consisted of an 8-bit cuboid volume using the same voxel-size and grid as real-world data with different sized cylindrical and cuboid-shaped cutouts (signal) whose grayscale values matched the real-world data set D1 (mean 68.7 ± 7.8; range 48.9–92.8). …”
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    VOLT: a novel open-source pipeline for automatic segmentation of endolymphatic space in inner ear MRI
  2. 5822
    “…The proposed model achieves a mean Dice coefficient index (DI) of 0.951 and a mean IOU of 0.909 that show how close the output segments are to the corresponding lesions in the ground truth maps. Data augmentation has been very effective in our experiments resulting in an increase in the mean DI and the mean IOU from 0.922 to 0.951 and 0.856 to 0.909, respectively. …”
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    Convolutional neural network for automated mass segmentation in mammography
  3. 5823
    “…OBJECTIVE: To evaluate a novel β-amyloid (Aβ)-PET–based quantitative measure (Aβ accumulation index [Aβ index]), including the assessment of its ability to discriminate between participants based on Aβ status using visual read, CSF Aβ(42)/Aβ(40), and post-mortem neuritic plaque burden as standards of truth. METHODS: One thousand one hundred twenty-one participants (with and without cognitive impairment) were scanned with Aβ-PET: Swedish BioFINDER, n = 392, [(18)F]flutemetamol; Alzheimer’s Disease Neuroimaging Initiative (ADNI), n = 692, [(18)F]florbetapir; and a phase 3 end-of-life study, n = 100, [(18)F]flutemetamol. …”
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    Derivation and utility of an Aβ-PET pathology accumulation index to estimate Aβ load
  4. 5824
    “…Serial annotation by 3 board-certified pathologists served as ground truth for model training and for evaluation. Images of kidney biopsy specimens were obtained from the Washington University database (retrieved between June 2015 and June 2017). …”
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    Development and Validation of a Deep Learning Model to Quantify Glomerulosclerosis in Kidney Biopsy Specimens
  5. 5825
    “…We aimed to determine the predictive values of visual assessments and Centiloids in non-demented patients, using long-term progression to dementia as our standard of truth. METHODS: One hundred sixty non-demented participants (age, 54–86) were enrolled in a monocentric [(18)F] flutemetamol Aβ-PET study. …”
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    Defining a Centiloid scale threshold predicting long-term progression to dementia in patients attending the memory clinic: an [(18)F] flutemetamol amyloid PET study
  6. 5826
    “…The MR sequences in the training set included pre-gadolinium and post-gadolinium (Gd) T1-weighted 3D fast spin echo, post-Gd T1-weighted inversion recovery (IR) prepped fast spoiled gradient echo, and 3D fluid attenuated inversion recovery (FLAIR), whereas the test set did not include the IR prepped image-series. The ground truth segmentations were established by experienced neuroradiologists. …”
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    Handling missing MRI sequences in deep learning segmentation of brain metastases: a multicenter study
  7. 5827
    “…We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. …”
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    Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images
  8. 5828
    “…Stored plasma samples were drawn from the TB Recurrence upon Treatment with HAART (TRuTH) study, in which individuals with previously treated pulmonary TB were screened for recurrence quarterly for up to 4 years. …”
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    Plasma Biomarkers of Risk of Tuberculosis Recurrence in HIV Co-Infected Patients From South Africa
  9. 5829
    “…Upon generating saliency maps that visualize the areas each model views as significant for prediction, models trained on less compressed (CR <  = 5 K) images had maps encapsulating a radiologist’s label, while models trained on images with higher amounts of compression had maps that missed the ground truth completely. In addition, base ResNet18 models pre-trained on ImageNet and trained using compressed mammograms did not show performance improvements over our CNN model, with AUROC and AUPRC values ranging from 0.77 to 0.87 and 0.52 to 0.71 respectively when trained and tested on images with maximum CRs of 5 K. …”
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    Impact of image compression on deep learning-based mammogram classification
  10. 5830
    “…Skeletal muscle at L3 vertebra was manually segmented by two observers, according to the Alberta protocol to serve as ground truth labels. This included 40 images segmented by both observers to measure inter-observer variation. …”
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    A Deep Learning Model to Automate Skeletal Muscle Area Measurement on Computed Tomography Images
  11. 5831
    “…The participants scored the highest on the CTDI-CV subscales of “inquisitiveness” and the lowest on “truth seeking.” The 112 students in the longitudinal study had significantly lower total CT scores after one academic year follow-up. …”
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    Chinese medical students’ disposition for critical thinking: a mixed methods exploration
  12. 5832
    “…Results were compared with those of manual liver volume quantification using the same software which was supervised by a senior radiologist with 30-year CT-experience (ground truth). RESULTS: In total, a correlation of 98% was obtained for liver volumetry based on enhanced and unenhanced data sets compared to the manual liver quantification. …”
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    Fully automated whole-liver volume quantification on CT-image data: Comparison with manual volumetry using enhanced and unenhanced images as well as two different radiation dose levels and two reconstruction kernels
  13. 5833
    “…To mitigate bias among multiple experts, the simultaneous truth and performance level estimation (STAPLE) algorithm was applied to create consensus maps. …”
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    Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary
  14. 5834
    “…A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. …”
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    COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models
  15. 5835
    “…Plant height and total plant surface area were validated against “ground truth” measurements, producing R(2) > 0.99 and a mean absolute percentage error < 10%. …”
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    Open source 3D phenotyping of chickpea plant architecture across plant development
  16. 5836
    “…We specified 15 criteria relating to data source, datasets, ground truth, outcomes, and reproducibility for quality assessment. …”
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    The reporting quality of natural language processing studies: systematic review of studies of radiology reports
  17. 5837
    “…The intervention was a mobile video-based education session (Truth About Low Back Pain) compared to usual care. …”
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    The influence of a MOBile-based video Instruction for Low back pain (MOBIL) on initial care decisions made by primary care providers: a randomized controlled trial
  18. 5838
    “…More specifically, Euler angle calculation algorithm in 2D (R(2) = 0.99) and in 3D (R(2) = 0.82 in yaw plane and R(2) = 0.91 for the pitch plane), as well as head turns speed (R(2) = 0.96), showed good correlation between the calculated and ground truth values provided by experts' annotations. The posture assessment algorithm resulted to accuracy = 0.83, while the gait metrics were validated against two well-established gait analysis systems (R(2) = 0.78 for double support, R(2) = 0.71 for single support, R(2) = 0.80 for step time, R(2) = 0.75 for stride time (WinTrack(©)), R(2) = 0.82 for cadence, and R(2) = 0.79 for stride time (RehaGait(©)). …”
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    Evaluating the Performance of Balance Physiotherapy Exercises Using a Sensory Platform: The Basis for a Persuasive Balance Rehabilitation Virtual Coaching System
  19. 5839
    “…Clinically, objective assessment of the TRE is impossible because establishing a sufficiently accurate ground-truth is impossible. A method was used to validate accuracy and usefulness based on intersubjectivity assessment of surgeon ratings of corresponding image-pairs from the microscope/endoscope and the image-guidance system. …”
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    Freehand Stereotactic Image-Guidance Tailored to Neurotologic Surgery
  20. 5840
    “…Our L3SEG-net was validated with internal (n = 496) and external validation (n = 586) datasets. Ground truth L3 level CT slice and anatomic variation were identified by a board-certified radiologist. …”
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    Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography
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