3D unsupervised anomaly detection through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomography

Author:

Kim KyungsuORCID,Oh Seong JeORCID,Lee Ju HwanORCID,Chung Myung JinORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference62 articles.

1. The medical segmentation decathlon;Antonelli,2021

2. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography;Ardila;Nature Medicine,2019

3. Reversing the abnormal: Pseudo-healthy generative networks for anomaly detection;Bercea,2023

4. Unsupervised detection of lung nodules in chest radiography using generative adversarial networks;Bhatt,2021

5. Comparison of helical, maximum intensity projection (MIP), and averaged intensity (AI) 4D CT imaging for stereotactic body radiation therapy (SBRT) planning in lung cancer;Bradley;Radiotherapy and Oncology,2006

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