AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality | Paper Citation Record

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AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Unresolved cited work

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AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Unresolved cited work

Reference 2

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AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality acceptable

Reference 3

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This paper cites Let 𝒴 denote the label space of ordinal quality levels, 𝒴 = {𝑟0, 𝑟1, … , 𝑟𝑘, … 𝑟𝐾−1} with 𝑟0 = 0, 𝑟𝑘 = 𝑘, 𝑘 = 1,2, … , 𝐾 − 1, and 𝑟𝐾−1 > ⋯ > 𝑟0.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Let 𝒴 denote the label space of ordinal quality levels, 𝒴 = {𝑟0, 𝑟1, … , 𝑟𝑘, … 𝑟𝐾−1} with 𝑟0 = 0, 𝑟𝑘 = 𝑘, 𝑘 = 1,2, … , 𝐾 − 1, and 𝑟𝐾−1 > ⋯ > 𝑟0

Reference 4

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AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Unresolved cited work

Reference 5

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This paper cites accuracy vs. uncertainty.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality accuracy vs. uncertainty

Reference 6

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This paper cites The CT scans from these patients had slice thickness ranging from 1.5 to 3.0 mm, resolution of 0.9 to 1.5 mm, image size of 512x512 pixels, and 90 to 290 slices per scan.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality The CT scans from these patients had slice thickness ranging from 1.5 to 3.0 mm, resolution of 0.9 to 1.5 mm, image size of 512x512 pixels, and 90 to 290 slices per scan

Reference 7

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This paper cites The model was trained using the Adam optimizer with default hyperparameters ( β1 = 0.9, and β2 = 0.999) over 1 × 105 iterations, leveraging the dice loss function.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality The model was trained using the Adam optimizer with default hyperparameters ( β1 = 0.9, and β2 = 0.999) over 1 × 105 iterations, leveraging the dice loss function

Reference 8

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This paper cites As shown in Table 2, the ROC AUC values increased from 0.821 to 0.932 and from 0.778 to 0.808 for predicting Class 2 and Class 1 , respectively.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality As shown in Table 2, the ROC AUC values increased from 0.821 to 0.932 and from 0.778 to 0.808 for predicting Class 2 and Class 1 , respectively

Reference 9

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AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Unresolved cited work

Reference 10

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This paper cites The results indicate an initially positive trend, with accuracy increasing as the sample size grows from 10 to 30.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality The results indicate an initially positive trend, with accuracy increasing as the sample size grows from 10 to 30

Reference 11

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This paper cites This case exhibits high uncertainty for the AI model; please review carefully.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality This case exhibits high uncertainty for the AI model; please review carefully

Reference 12

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This paper cites Exploring the cancer patients’ experiences during external radiotherap y: A sys- tematic review and thematic synthesis of qualitative and quantitative evidence,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Exploring the cancer patients’ experiences during external radiotherap y: A sys- tematic review and thematic synthesis of qualitative and quantitative evidence,

Reference 13

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This paper cites Online adaptive radiotherapy potentially reduces toxicity for high-risk prostate cancer treatment,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Online adaptive radiotherapy potentially reduces toxicity for high-risk prostate cancer treatment,

Reference 14

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This paper cites Metrics to evaluate the performance of auto -segmentation for radiation treatment planning: A critical review,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Metrics to evaluate the performance of auto -segmentation for radiation treatment planning: A critical review,

Reference 16

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This paper cites A framework for automated contour quality assurance in radiation therapy including adaptive techniques,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality A framework for automated contour quality assurance in radiation therapy including adaptive techniques,

Reference 17

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This paper cites Contouring quality assurance methodology based on multiple geometric features against deep learning auto‐segmenta- tion,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Contouring quality assurance methodology based on multiple geometric features against deep learning auto‐segmenta- tion,

Reference 18

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This paper cites Comprehensive Clinical Usability-Oriented Contour Quality Evaluation for Deep Learning Auto-segmentation: Com- bining Multiple Quantitative Metrics Through Machine Learning,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Comprehensive Clinical Usability-Oriented Contour Quality Evaluation for Deep Learning Auto-segmentation: Com- bining Multiple Quantitative Metrics Through Machine Learning,

Reference 19

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This paper cites Automated Quality Assurance of OAR Contouring for Lung Cancer Based on Segmentation With Deep Active Learning,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Automated Quality Assurance of OAR Contouring for Lung Cancer Based on Segmentation With Deep Active Learning,

Reference 20

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This paper cites CNN-Based Quality Assurance for Automatic Segmentation of Breast Cancer in Radiotherapy,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality CNN-Based Quality Assurance for Automatic Segmentation of Breast Cancer in Radiotherapy,

Reference 21

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This paper cites Uncertainty Assessment for Deep Learning Radiotherapy Applications,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Uncertainty Assessment for Deep Learning Radiotherapy Applications,

Reference 22

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This paper cites Towards reliable head and neck cancers locoregional recurrence prediction using delta-radiomics and learning with rejection option,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Towards reliable head and neck cancers locoregional recurrence prediction using delta-radiomics and learning with rejection option,

Reference 23

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This paper cites Predicting lymph node metastasis in patients with oropharyngeal cancer by using a convolutional neural network with associated epistemic and aleatoric uncertainty,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Predicting lymph node metastasis in patients with oropharyngeal cancer by using a convolutional neural network with associated epistemic and aleatoric uncertainty,

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This paper cites Uncertainty estimations methods for a deep learning model to aid in clinical decision -making – a clinician’s perspective.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Uncertainty estimations methods for a deep learning model to aid in clinical decision -making – a clinician’s perspective

Reference 25

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This paper cites A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy,

Reference 26

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This paper cites Uncertainty quantification using Bayesian neural networks in classification: Applica- tion to biomedical image segmentation,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Uncertainty quantification using Bayesian neural networks in classification: Applica- tion to biomedical image segmentation,

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This paper cites Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation

Reference 28

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This paper cites Using Spatial Probability Maps to Hi ghlight Potential Inaccuracies in Deep Learning -Based Contours: Facilitating Online Adaptive Radiation Therapy - ClinicalKey.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Using Spatial Probability Maps to Hi ghlight Potential Inaccuracies in Deep Learning -Based Contours: Facilitating Online Adaptive Radiation Therapy - ClinicalKey

Reference 29

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This paper cites A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks,

Reference 30

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This paper cites Uncertainty in Multitask Learning: Joint Representations for Probabilistic MR -only Radiotherapy Planning,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Uncertainty in Multitask Learning: Joint Representations for Probabilistic MR -only Radiotherapy Planning,

Reference 31

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This paper cites Deep learning based synthetic-CT generation in radiotherapy and PET: A review,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Deep learning based synthetic-CT generation in radiotherapy and PET: A review,

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This paper cites Deep Generative Model for Synthetic-CT Generation with Uncertainty Predictions,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Deep Generative Model for Synthetic-CT Generation with Uncertainty Predictions,

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This paper cites Uncertainty in Deep Learning,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Uncertainty in Deep Learning,

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This paper cites Deep neural networks for rank -consistent ordinal regression based on conditional probabilities,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Deep neural networks for rank -consistent ordinal regression based on conditional probabilities,

Reference 35

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This paper cites Clinical evaluation of a deep learning segmentation model including manual adjustments afterwards for locally advanced breast cancer,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Clinical evaluation of a deep learning segmentation model including manual adjustments afterwards for locally advanced breast cancer,

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This paper cites Automated Contouring and Planning in Radiation Therapy: What Is ‘Clinically Acceptable’?,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Automated Contouring and Planning in Radiation Therapy: What Is ‘Clinically Acceptable’?,

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This paper cites Incremental retraining, clinical implementation, and acceptance rate of deep learning auto‐segmentation for male pelvis in a multiuser environment,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Incremental retraining, clinical implementation, and acceptance rate of deep learning auto‐segmentation for male pelvis in a multiuser environment,

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This paper cites Evaluating the clinical acceptability of deep learning contours of prostate and organs -at-risk in an automated prostate treatment planning process,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Evaluating the clinical acceptability of deep learning contours of prostate and organs -at-risk in an automated prostate treatment planning process,

Reference 39

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Observation f0d21906-4075-4356-95a9-b8a1a0f686ba · outbound

This paper cites Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy,

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This paper cites Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

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This paper cites A systematic review of deep learning data augmentation in medical imaging: Recent advances and future research directions,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality A systematic review of deep learning data augmentation in medical imaging: Recent advances and future research directions,

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This paper cites Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning,.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning,

Reference 43

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