“…We used RT-qPCR as the ground truth to compare with the thermal parameters, in order to evaluate the performance of infrared imaging in COVID-19 screening. …”
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“…(1) Background: To evaluate the performance of a deep learning model to automatically segment femoral head necrosis (FHN) based on a standard 2D MRI sequence compared to manual segmentations for 3D quantification of FHN. (2) Methods: Twenty-six patients (thirty hips) with avascular necrosis underwent preoperative MR arthrography including a coronal 2D PD-w sequence and a 3D T1 VIBE sequence. Manual ground truth segmentations of the necrotic and unaffected bone were then performed by an expert reader to train a self-configuring nnU-Net model. …”
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“…As computational metrics for evaluation do not directly translate to clinical settings, we involve expert clinicians in designing evaluation metrics that test four properties: safety, logical coherence, and knowledge capture for explainability while minimizing the standard cross entropy loss to preserve distribution semantics-based similarity to the ground truth. LMs with ProKnow-algo generated 89% safer questions in the depression and anxiety domain (tested property: safety). …”
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“…The segmentations were derived with FreeSurfer from the non‐enhanced image and used as ground truth for the coregistered CE image. A longitudinal dataset of patients with multiple sclerosis (MS), comprising relapsing remitting (RRMS) and primary progressive (PPMS) subgroups, was used for the evaluation. …”
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“…The results showed that the R(2) between the number predicted by YOLO POD and the ground truth reached 0.967, which is improved by 0.049 compared to YOLO X, while the inference time only increased by 0.08 s. …”
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“…The average intersection over union (IOU) between CNN2 and the ground truth was 0.5837, with a precision, recall and the Dice coefficient of 0.776, 0.983 and 0.867, respectively. …”
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“…Study aims focused on disclosure of diagnosis (27%) or prognosis (20%), breaking bad news (15%), general patient-clinician communication (12%), truth-telling (8%), shared decision-making (7%), information needs/preferences (5%), and/or advance care planning (5%). …”
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“…Cohort-specific ROIs were combined into single simultaneous truth and performance level estimation (STAPLE) consensus segmentations. …”
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“…In an attempt to suppress this fear, the postmodern human contests, in various ways, the truth of their ontic frailty and the fragility of their constructed “happiness”. …”
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“…All measurements were performed in situ and a refractometer was used to measure the total soluble solids content (°Brix) of the grapes, providing the ground truth data. After the development of the grape spectra library, four different machine learning algorithms, namely Partial Least Squares regression (PLS), Random Forest regression, Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), coupled with several pre-treatment methods were applied for the prediction of the °Brix content from the VNIR–SWIR hyperspectral data. …”
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“…Five canonical CNN segmentation models were evaluated using both T1‐ and T2‐weighted images of 23 6‐month‐old infants from iSeg‐2019 datasets, which contained manual labels as ground truth. MixACB significantly enhanced the average accuracy of all five models and obtained the most considerable improvement in the fully convolutional network model (CC‐3D‐FCN) and the highest performance in the Dense U‐Net model. …”
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“…We think dialogics engender a mutual understanding that: i) transitions beliefs beyond bias, ii) moves conflict toward pragmatism (i.e., the truth of any position is verified by subsequent experience), and iii) recognizes value pluralism (i.e., human values are irreducibly diverse, conflicting, and ultimately incommensurable). …”
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“…To evaluate the quality of the agreement between the model predictions and the ground truth, we must keep in mind that our gold standard relies on a single observer's (CB) manually-delineated tumour contours. …”
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“…Pathologic assessment with immunohistochemistry (PSMA-staining and SSA immunoreactivity) was considered as standard of truth. Specimen images were compared with baseline PET/CT images and histopathological analysis. …”
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“…Whole-mount RP histopathology will provide the ground truth to train the image classification algorithm. …”
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“…Information about the performance metrics of assays measuring IFN-I and measures of truth were extracted and summarised. A EULAR task force panel assessed feasibility and developed consensus terminology. …”
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“…Results were robust to the modality used to acquire images (PET/CT or PET/MRI) and to the ground truth labels (no significant difference between the model’s performance when compared to reader 1 or reader 2 manual contouring). …”
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“…Good agreement was obtained in comparing the results of these methods with the estimations from Monte-Carlo truth. The large statistics expected in early data at low luminosity from J/psi and Upsilon to ee decays will extend the use of this method over the full pT range of interest for searches (Higgs-boson decays to electrons, electrons produced in cascade decays of SUSY particles, etc).…”
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“…In the hardware L1 Trigger case study this could be from the unbiased 40 MHz scouting data that is used as truth level training data for a model. This would be a solution to the fill-level changes in detector conditions where generating a large MC dataset is not feasible. …”
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“…Evaluation of the additional diagnostic value of the synthetic protocol was performed by calculation of Cohen’s ĸ and accuracy in comparison to a ground truth (GT) grading based on real T2-w fs images, pre- or follow-up scans, other imaging modalities, and clinical information. (3) Results: The addition of the synthetic T2-w fs to the imaging protocol led to a more precise grading of abnormalities than when grading was based on T1-w and non-fs T2-w images only (mean ĸ GT versus synthetic protocol = 0.65; mean ĸ GT versus T1/T2 = 0.56; p = 0.043). (4) Conclusions: The implementation of synthetic T2-w fs images in the radiological workflow significantly improves the overall assessment of spine pathologies. …”
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