Nassir Navab

Self-supervised depth estimation gets a boost: SA4Depth aligns scene scales between pose and depth networks, leading to substantial improvements in depth prediction without sacrificing inference speed.

A simple "resilience" metric turns out to be surprisingly effective at identifying failure cases in NCA-based medical image segmentation, improving trust without retraining.

Forget full automation; the future of medical robotics is "Dyadic Partnership," where AI and clinicians collaborate as equals, leveraging generative AI and intuitive interfaces for shared decision-making.

Ditch anatomical segmentations: this method tracks disease progression by watching how MRI sequence vectors "flow" across a baseline energy landscape learned from a single scan.

Surgical robots can now reason about 4D spatiotemporal relationships in laparoscopic videos, without any additional training, by simply combining existing 2D MLLMs with a novel 3D computer vision pipeline.

Radiology report generation models can now verbalize calibrated confidence estimates, enabling targeted radiologist review of potentially hallucinated findings.

An RL-aligned LLM can outperform expert toxicologists in identifying ingested substances from heterogeneous clinical data, suggesting a path to AI-assisted decision-making in high-stakes medical environments.

Forget brittle, fixed robotic US scanning procedures: this LLM-powered agent dynamically interprets guidelines and adapts to real-time observations, enabling autonomous scanning across diverse anatomical targets.

Instruction-tuned LLMs can mine free-text radiology reports to create a knowledge base that significantly improves the accuracy of structured report generation, especially for rare and detailed findings.

Ditch the flat scene graphs: TopoOR models surgical environments as higher-order topological structures, unlocking superior performance in safety-critical tasks by preserving complex relationships and multimodal data.

Achieve 80% better shape completion for ultrasound reconstruction by implicitly modeling acoustic interactions, eliminating the need for anatomical labels during inference.

Achieve robust dual-arm robotic ultrasound-guided interventions by learning personalized expert strategies from limited demonstrations using a phase-aware imitation learning policy.

By mimicking how pathologists dynamically integrate global and cellular evidence across multiple magnifications, MMNavAgent significantly boosts WSI diagnostic performance.

Achieve autonomous robotic ultrasound examinations with RAG-RUSS, an interpretable framework that explains its actions and generalizes well even with limited training data.