Picture for Dan Nguyen

Dan Nguyen

Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, UT Southwestern Medical Center, Dallas TX 75235, USA

Can input reconstruction be used to directly estimate uncertainty of a regression U-Net model? -- Application to proton therapy dose prediction for head and neck cancer patients

Oct 30, 2023
Figure 1 for Can input reconstruction be used to directly estimate uncertainty of a regression U-Net model? -- Application to proton therapy dose prediction for head and neck cancer patients
Figure 2 for Can input reconstruction be used to directly estimate uncertainty of a regression U-Net model? -- Application to proton therapy dose prediction for head and neck cancer patients
Figure 3 for Can input reconstruction be used to directly estimate uncertainty of a regression U-Net model? -- Application to proton therapy dose prediction for head and neck cancer patients
Figure 4 for Can input reconstruction be used to directly estimate uncertainty of a regression U-Net model? -- Application to proton therapy dose prediction for head and neck cancer patients
Viaarxiv icon

Thalamic nuclei segmentation from T$_1$-weighted MRI: unifying and benchmarking state-of-the-art methods with young and old cohorts

Sep 26, 2023
Viaarxiv icon

Deep Learning (DL)-based Automatic Segmentation of the Internal Pudendal Artery (IPA) for Reduction of Erectile Dysfunction in Definitive Radiotherapy of Localized Prostate Cancer

Feb 03, 2023
Viaarxiv icon

Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation

Nov 19, 2022
Figure 1 for Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation
Figure 2 for Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation
Figure 3 for Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation
Figure 4 for Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation
Viaarxiv icon

Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy

Oct 11, 2022
Figure 1 for Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy
Figure 2 for Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy
Figure 3 for Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy
Figure 4 for Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy
Viaarxiv icon

Uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective

Add code
Oct 02, 2022
Figure 1 for Uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective
Figure 2 for Uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective
Figure 3 for Uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective
Figure 4 for Uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective
Viaarxiv icon

Exploring the combination of deep-learning based direct segmentation and deformable image registration for cone-beam CT based auto-segmentation for adaptive radiotherapy

Jun 07, 2022
Figure 1 for Exploring the combination of deep-learning based direct segmentation and deformable image registration for cone-beam CT based auto-segmentation for adaptive radiotherapy
Figure 2 for Exploring the combination of deep-learning based direct segmentation and deformable image registration for cone-beam CT based auto-segmentation for adaptive radiotherapy
Figure 3 for Exploring the combination of deep-learning based direct segmentation and deformable image registration for cone-beam CT based auto-segmentation for adaptive radiotherapy
Figure 4 for Exploring the combination of deep-learning based direct segmentation and deformable image registration for cone-beam CT based auto-segmentation for adaptive radiotherapy
Viaarxiv icon

Region Specific Optimization (RSO)-based Deep Interactive Registration

Add code
Mar 08, 2022
Figure 1 for Region Specific Optimization (RSO)-based Deep Interactive Registration
Figure 2 for Region Specific Optimization (RSO)-based Deep Interactive Registration
Figure 3 for Region Specific Optimization (RSO)-based Deep Interactive Registration
Figure 4 for Region Specific Optimization (RSO)-based Deep Interactive Registration
Viaarxiv icon

OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines

Add code
Feb 16, 2022
Figure 1 for OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines
Figure 2 for OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines
Figure 3 for OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines
Figure 4 for OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines
Viaarxiv icon

Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy

Add code
Feb 08, 2022
Figure 1 for Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy
Figure 2 for Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy
Figure 3 for Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy
Figure 4 for Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy
Viaarxiv icon