Tag: Endoscopy

Our lab membersโ€™ achievements at the ๐— ๐—ฅ๐—– ๐—ฆ๐˜†๐—บ๐—ฝ๐—ผ๐˜€๐—ถ๐˜‚๐—บ ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ

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๐Ÿ† ๐‘ฉ๐’†๐’”๐’• ๐‘ซ๐’†๐’”๐’Š๐’ˆ๐’ ๐‘จ๐’˜๐’‚๐’“๐’…

Tinghua Zhang, Sishen YUAN et al. for “PneumaOCT: Pneumatic optical coherence tomography endoscopy for targeted distortion-free imaging in tortuous and narrow internal lumens”, a collaboration between CUHK ABI Lab (https://lnkd.in/gUuzQqDt) and RENLab (labren.org),

published in Science Advances (DOI: 10.1126/sciadv.adp3145).

๐Ÿ”ฌ ๐‘ฉ๐’†๐’”๐’• ๐‘จ๐’‘๐’‘๐’๐’Š๐’„๐’‚๐’•๐’Š๐’๐’ ๐‘จ๐’˜๐’‚๐’“๐’…

Dr. Mengya Xu, Wenjin Mo et al. for their work:

“ETSM: Automating Dissection Trajectory Suggestion and Confidence Map-Based Safety Margin Prediction for Robot-assisted Endoscopic Submucosal Dissection”, accepted at #ICRA2025 (arXiv preprint: arXiv:2411.18884).

๐ŸŒŸ Congratulations to our brilliant team members on these well-deserved recognitions!

Additionally, Prof. Hongliang Ren delivered an insightful talk, “Endoscopic Multisensory Navigation with Soft Flexible Robotics”, highlighting the latest advancements in endoscopic navigation and soft medical robotics.

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๐ŸŽ‰ Check out our #MICCAI2024 accepted paper “EndoUIC: Promptable Diffusion Transformer for Unified Illumination Correction in Capsule Endoscopy”.

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In this work, through incorporating a set of learnable parameters to prompt the learning targets, the diffusion model can effectively address the unified illumination correction challenge in capsule endoscopy. We also propose a new capsule endoscopy dataset including underexposed and overexposed images, as well as the ground truth.

Thanks to all of our collaborators from multiple institutions: Long Bai, Qiaozhi Tan, Zhicheng He, Sishen YUAN, Prof. Hongliang Ren from CUHK & SZRI, Tong Chen from USYD, Wan Jun Nah from Universiti Malaya, Yanheng Li from CityU HK, Prof. Zhen CHEN, Prof. Jinlin Wu, Prof. Hongbin Liu from CAIR HK, Dr. Mobarakol Islam from WEISS, UCL, and Dr. Zhen Li from Qilu Hospital of SDU.

Paper: https://lnkd.in/gJaqikqj

Code & Dataset: https://lnkd.in/ghYauAGM

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