No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks

8 March 2020

Papers citing "No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks"

5 / 5 papers shown

Title
MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack Mengting Xu Tao Zhang Daoqiang Zhang AAML MedIm 13 23 0 29 Nov 2021
Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges Kashif Ahmad Majdi Maabreh M. Ghaly Khalil Khan Junaid Qadir Ala I. Al-Fuqaha 27 142 0 14 Dec 2020
3D Tomographic Pattern Synthesis for Enhancing the Quantification of COVID-19 Siqi Liu Bogdan Georgescu Zhoubing Xu Y. Yoo G. Chabin ... Brian Teixeira A. Balachandran R. Vishwanath Thomas J. Re D. Comaniciu 33 18 0 05 May 2020
Disentangling Adversarial Robustness and Generalization David Stutz Matthias Hein Bernt Schiele AAML OOD 186 272 0 03 Dec 2018
CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation D. Jin Ziyue Xu Youbao Tang Adam P. Harrison D. Mollura MedIm 49 185 0 11 Jun 2018