Signals 4 · free daily AI digest

Quantifying privacy risks in foundation model representations of brain MRI

Paper recorded by Signals 4 on 2026-10-14 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-14 on arXiv · recorded by Signals 4 on 2026-10-15

Category: cs.CV · 计算机视觉 · first seen 2026-10-15

Abstract

Pretrained representations are increasingly used to share and reuse information from medical images without exposing the original scans, but it is unclear how much patient-specific information these representations retain. We investigate this question for brain magnetic resonance imaging (MRI) by evaluating five frozen pretrained encoders spanning different architectures, pretraining objectives, a

Read on arXiv →

#276 most recent of 756 cs.CV papers we have recorded · ↑ newer: OLiVES: An Outdoor Low-Light Video Benchmark for Enhancement and Segme · ↓ older: LoopWAM: Closed-Loop Generalist Policy Improvement from Deployment Rol
Cite this page: Quantifying privacy risks in foundation model representations of brain MRI: the #276 most recent of 756 cs.CV papers we have recorded (as of 2026-10-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/quantifying-privacy-risks-in-foundation-model-representations-of-brain-mri.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.CV papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions