arXiv Machine Learning
Aug 20

Progressive Experience Fusion for Multi-Task World Model Control in Endovascular Navigation

The paper introduces Progressive Experience Fusion (PEF) for training a multi-task TD-MPC2 controller to navigate endovascular paths across diverse vascular anatomies. PEF outperforms Soft Actor-Critic and base TD-MPC2, achieving 74% success on training anatomies and 90% on held‑out vasculatures. The controller also transfers to an unseen in‑vitro stroke patient, improving path ratio from 63% to 80% after fine‑tuning.

By Harry Robertshaw, Maxence Boels, Nikola Fischer, Sebastien Ourselin, Christos Bergeles, Alejandro Granados, Thomas C Booth
arXiv AI
Jul 14

Toward Autonomous Soft Robotic Endovascular Navigation via Imitation Learning

arXiv:2510. 09497v2 Announce Type: replace-cross Abstract: In endovascular surgery, endovascular interventionists push a thin tube called a catheter, guided by a thin wire to a treatment site inside the patient's blood vessels to treat various conditions such as blood clots, aneurysms, and malformations.

By Noah Barnes, Ji Woong Kim, Lingyun Di, Hannah Qu, Anuruddha Bhattacharjee, Miroslaw Janowski, Dheeraj Gandhi, Bailey Felix, Shaopeng Jiang, Olivia Young, Mark Fuge, Ryan D. Sochol, Jeremy D. Brown, Axel Krieger
arXiv Machine Learning
Sep 2

Lightweight Adaptation of EEG Foundation Models for Stroke Motor Imagery Decoding: Domain Shift and Subject-Level Robustness

The study investigates whether Low‑Rank Adaptation (LoRA) can adapt three pretrained EEG foundation models—LaBraM‑base, REVE‑base, and REVE‑large—for binary left‑ vs. right‑hand motor imagery decoding in stroke patients. Using subject‑wise five‑fold cross‑validation on the PhysioNet EEG Motor Movement/Imagery Dataset and a binary subset of the UET175 stroke dataset, LoRA significantly improved accuracy for LaBraM‑base (0.822) and REVE‑base (0.957) on the healthy cohort, but only REVE‑base LoRA achieved high performance (0.847±0.194) on stroke data, with a best mean accuracy of 0.952 in leave‑one‑subject‑out evaluation. The results demonstrate that healthy‑benchmark performance does not guarantee transfer to stroke EEG, highlighting the need for target‑domain adaptation and subject‑level assessment in rehabilitation BCIs.

By Anh T. Nguyen, Zihua Sun, Michelle J. Johnson