2021 AAPM Virtual 63rd Annual Meeting - Session: AI in Imaging
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Automated Tumor Localization and Segmentation Through Hybrid Neural Network in Head & Neck Cancer Ahmad Qasem |
All videos in this session:
An Effective Deep Learning Framework for Lung Tumor Segmentation in 4D-CT - Shadab Momin Emory University | |
Deep Siamese Network for False Positive Reduction in Brain Metastases Segmentation - Zi Yang UT Southwestern Medical Center | |
Development of Artificial Intelligence (AI) Based Platform for Locally Advanced Rectal Cancer Prognosis - Yang Zhang Rutgers Cancer Institute of New Jersey | |
Unsupervised COVID-19 Pneumonia Lesion Segmentation in CT Scans Using Cycle Consistent Generative Adversarial Network - Yingao Liu university of science and technology of china | |
BEST IN PHYSICS (IMAGING): Validation of a Deep-Learning Model Observer in a Realistic Lung-Nodule Detection Task with Convolutional Neural Network-Based CT Denoising - Hao Gong Mayo Clinic | |
Towards Understandable AI in Lung Nodule Detection: Using the Genetic Algorithm for Interpretable, Human-Understandable Optimization of Nodule Candidate Generation in Lung CT Imaging - Muhammad Wahi-Anwar UCLA | |
Q & A - |





















An Effective Deep Learning Framework for Lung Tumor Segmentation in 4D-CT
Deep Siamese Network for False Positive Reduction in Brain Metastases Segmentation
Development of Artificial Intelligence (AI) Based Platform for Locally Advanced Rectal Cancer Prognosis
Unsupervised COVID-19 Pneumonia Lesion Segmentation in CT Scans Using Cycle Consistent Generative Adversarial Network
BEST IN PHYSICS (IMAGING): Validation of a Deep-Learning Model Observer in a Realistic Lung-Nodule Detection Task with Convolutional Neural Network-Based CT Denoising
Towards Understandable AI in Lung Nodule Detection: Using the Genetic Algorithm for Interpretable, Human-Understandable Optimization of Nodule Candidate Generation in Lung CT Imaging
Q & A