all AI news
Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis. (arXiv:2209.04338v1 [eess.IV])
Sept. 12, 2022, 1:11 a.m. | Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis
cs.LG updates on arXiv.org arxiv.org
Machine learning with formal privacy-preserving techniques like Differential
Privacy (DP) allows one to derive valuable insights from sensitive medical
imaging data while promising to protect patient privacy, but it usually comes
at a sharp privacy-utility trade-off. In this work, we propose to use steerable
equivariant convolutional networks for medical image analysis with DP. Their
improved feature quality and parameter efficiency yield remarkable accuracy
gains, narrowing the privacy-utility gap.
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
IT Commercial Data Analyst - ESO
@ National Grid | Warwick, GB, CV34 6DA
Stagiaire Data Analyst – Banque Privée - Juillet 2024
@ Rothschild & Co | Paris (Messine-29)
Operations Research Scientist I - Network Optimization Focus
@ CSX | Jacksonville, FL, United States
Machine Learning Operations Engineer
@ Intellectsoft | Baku, Baku, Azerbaijan - Remote
Data Analyst
@ Health Care Service Corporation | Richardson Texas HQ (1001 E. Lookout Drive)