OPERA: Multi-Agent Framework for Biomedical Image Analysis Without Retraining
UW · hf · 2026-07-30
Introduces OPERA (Offline Policy-guided Expert Routing and Adaptation), a multi-agent ensemble framework designed to tackle severe distribution shifts in real-world biomedical image analysis (e.g., across different scanners, protocols, and patient populations).
Instead of costly domain-specific fine-tuning, OPERA treats expert weight assignment as an offline policy learning problem. A routing policy is learned from a small validation set without gradient updates to any expert agent, which is then deployed with test-time adaptation to handle distribution shifts.
Evaluated on 9 datasets covering fundus photography, X-ray, CT, and MRI, OPERA outperforms 30+ baselines across classification, segmentation, and multimodal settings, proving to be a practical path to deployable biomedical AI without retraining.
More from Research
- NeurIPS Reviewer Ghosting: How to Handle Ignored Rebuttals — grumpket · 2026-07-30
- AIPOCH Open-Sources Library of 550+ Medical Research Agent Skills — tom_doerr · 2026-07-30
- ICSE'26 Paper Proposes New Paradigm: In-vivo Fuzzing Without Test Harnesses — moarbugs · 2026-07-30
- LMSYS Debuts Miles: Blackwell-Native 8-bit and 4-bit RL Recipes — BanghuaZ · 2026-07-30
- NeurIPS 2026 Workshop Tackles Continual Learning in Deployed AI Agents — DanielKhashabi · 2026-07-30
- MONTREAL.AI Releases 53-Page Paper: A Framework for Forecasting an Accelerating AI World — Ghost_Pilot_MD · 2026-07-30