Category: News

The paper is accepted for presentation at IPCAI2025, and we’re especially humbled to receive the 𝗹𝗛𝗨 𝗦𝘁𝗿𝗮𝘀𝗯𝗼𝘂𝗿𝗴 𝗮𝗻𝗱 𝗡𝗗𝗹 𝗕𝗲𝗻𝗰𝗵 𝘁𝗼 𝗕𝗲𝗱𝘀𝗶𝗱𝗲 𝗔𝘄𝗮𝗿𝗱: 𝗛𝗼𝗻𝗼𝗿𝗮𝗯𝗹𝗲 𝗠𝗲𝗻𝘁𝗶𝗼𝗻.

In this work, we address the challenge of accurately delineating the dissection zones during endoscopic submucosal dissection procedures. By integrating flexible visual cues—such as scribbles and bounding boxes—directly onto surgical images, our PDZSeg model guides segmentation for both better precision and enhanced safety. Leveraging a state-of-the-art foundation model (DINOv2) and an efficient LoRA training strategy, we fine-tuned our approach on the specialized ESD-DZSeg dataset. Our experimental results show promising improvements over traditional methods, offering robust support for intraoperative guidance and remote surgical training.

Our sincere thanks to every colleague (Mengya Xu, Wenjin Mo, Guankun Wang, Huxin Gao, An Wang), mentor (Dr. Ning Zhong, Dr. Zhen Li, Dr. Xiaoxiao Yang, Prof. Hongliang Ren), and community member whose support has been indispensable. This achievement reaffirms our collective effort and inspires us to further refine robotic-assisted techniques towards enhanced safety and effectiveness.

Paper available at: https://lnkd.in/g7KcytnE

Dr. Yuan’s research advances the field of medical robotics, paving the way for innovative healthcare solutions. A well-deserved milestone after years of dedication and research!

Special thanks to his supervisor: Prof. Hongliang Ren and Prof. Max Q.-H. Meng, as well as the examining committee members: Prof. Gao Shichang, Prof. Wu ‘Scott’ YUAN, and Prof. Zhidong Wang for their invaluable guidance and support.

🔗 Learn more about Dr. Yuan’s research on https://lnkd.in/g7GVhwtv.

“𝐿𝑜𝑤-𝑆𝑡𝑟𝑎𝑖𝑛 𝐹𝑙𝑒𝑥𝑖𝑏𝑙𝑒 𝐼𝑛𝑘-𝐵𝑎𝑠𝑒𝑑 𝑆𝑒𝑛𝑠𝑜𝑟𝑠 𝐸𝑛𝑎𝑏𝑙𝑒 𝐻𝑦𝑝𝑒𝑟𝑒𝑙𝑎𝑠𝑡𝑖𝑐 𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛 𝑃𝑒𝑟𝑐𝑒𝑝𝑡𝑖𝑜𝑛 𝑉𝑖𝑎 𝐺𝑒𝑜𝑚𝑒𝑡𝑟𝑖𝑐 𝑃𝑎𝑡𝑡𝑒𝑟𝑛𝑖𝑛𝑔”

📄 What we explored:

– Designed Ω-shaped flexible sensors using conductive ink to reduce strain mismatches on inflatable robots

– Developed a light-curing transfer method for precise sensor attachment

– Tested integration with balloon-type robots showing improved deformation tracking at >300% expansion

🩺 Our sensor system has the potential to enable:

🔹 Real-time inflation tracking for safer human-robot interaction

🔹 Spatial perception in biomedical devices (catheters, surgical tools)

🔹 5x reduction in circumference/area error vs. conventional designs

Congrats to the authors: WENCHAO YUE, Shuoyuan Chen, Yan Ke, Yingyi Wen, Ruijie Tang, Guohua Hu, and Hongliang Ren.

📄 Paper Open Access at: https://lnkd.in/gm9DRybP

We extend our sincere gratitude to Prof. Tan Lee, Prof. Qi Dou, and Prof. S. Kevin Zhou for serving as examiners during Long Bai’s defense. Special thanks to his supervisors, Prof. Hongliang Ren and Prof. Jiewen Lai, for their invaluable guidance throughout his Ph.D. journey.

During his time at CUHK RenLab, Dr. Bai has made impressive contributions to surgical and medical artificial intelligence, particularly in multimodal AI.

🔗 For more details about his research, visit his personal website: longbai-cuhk.github.io.

Wishing Dr. Long Bai all the best in his future endeavors! 🚀👏

🗞️ We propose a multimodal Graph Representation network with Adversarial feature Disentanglement (GRAD) for robust surgical workflow recognition in challenging scenarios with domain shifts or corrupted data. Specifically, we introduce a Multimodal Disentanglement Graph Network (MDGNet) that captures fine-grained visual information while explicitly modeling the complex relationships between vision and kinematic embeddings through graph-based message modeling. To align feature spaces across modalities, we propose a Vision-Kinematic Adversarial (VKA) framework that leverages adversarial training to reduce modality gaps and improve feature consistency. Furthermore, we design a Contextual Calibrated Decoder, incorporating temporal and contextual priors to enhance robustness against domain shifts and corrupted data.

🗞️ Extensive comparative and ablation experiments demonstrate the effectiveness of our model and proposed modules. Specifically, we achieved an accuracy of 86.87% and 92.38% on two public datasets, respectively. Moreover, our robustness experiments show that our method effectively handles data corruption during storage and transmission, exhibiting excellent stability and robustness. Our approach aims to advance automated surgical workflow recognition, addressing the complexities and dynamism inherent in surgical procedures.

📑 Paper link: https://lnkd.in/g9SWYPJg

👏 👏 We extend our gratitude to our co-authors and collaborators from around the world, including Long Bai, Boyi Ma, Ruohan Wang, Guankun Wang, Beilei Cui, Zhongliang Jiang, Mobarakol Islam, Zhe Min, Jiewen Lai, Nassir Navab, and Hongliang Ren!

This conference serves as a platform for researchers and practitioners to discuss advancements, challenges, and opportunities in information, automation, artificial intelligence, robotics, image processing, computer vision, DSP, and BME.

📅 Submission Deadline: June 15, 2025 🔗

Conference Website: http://www.icia2025.org/

In this work, we tackled a long‐standing challenge in soft tactile sensing—accurately localizing a contact point on a stretchable sensor even in the presence of strain and variable contact forces. Our approach uses ultrasonic scatter signals extracted from a soft waveguide to decouple these intertwined effects. A data-driven method was developed, combining:

– Global feature extraction: Using the Hilbert transform to capture the overall energy distribution before and after force contact.

– Local feature extraction: Leveraging continuous wavelet transforms (CWT) to retrieve high-resolution time–frequency characteristics.

– Deep learning integration: Fusing these features through a deep convolutional neural network and multilayer perceptron regression, which allowed us to achieve a mean absolute error of just 0.627 mm and a mean relative error of 3.19%.

This fusion of global and local signal analysis not only overcomes limitations of traditional time-of-flight estimation methods but also paves the way for more robust multimodal sensing in robotics and human–machine interfaces. The implications for advanced robotics, intelligent prosthetics, and other emerging applications are truly exciting.

Authors: Zhiheng Li, Yuan Lin, Peter Shull, Hongliang Ren

The paper is available at https://lnkd.in/dHHgw4qp

1)     Three-Dimension Tip Force Perception and Axial Contact Location Identification for Flexible Endoscopy Using Tissue-Compliant Soft Distal Attachment Cap Sensors
2)     TASEC: Rescaling Acquisition Strategy with Energy Constraints under Fusion Kernel for Active Incision Recommendation in Tracheotomy
3)     ETSM: Automating Dissection Trajectory Suggestion and Confidence Map-Based Safety Margin Prediction for Robot-Assisted Endoscopic Submucosal Dissection
4)     Advancing Dense Endoscopic Reconstruction with Gaussian Splatting-Driven Surface Normal-Aware Tracking and Mapping
5)     Minimally Invasive Endotracheal Inside-Out Flexible Needle Driving System towards Microendoscope-Guided Robotic Tracheostomy
6)     Variable-Stiffness Nasotracheal Intubation Robot with Passive Buffering: A Modular Platform in Mannequin Studies
7)     SurgPLAN++: Universal Surgical Phase Localization Network for Online and Offline Inference
Looking forward to catching up!