🎉 Excited to share that our paper “𝐂𝐨𝐏𝐄𝐒𝐃: 𝐀 𝐌𝐮𝐥𝐭𝐢-𝐋𝐞𝐯𝐞𝐥 𝐒𝐮𝐫𝐠𝐢𝐜𝐚𝐥 𝐌𝐨𝐭𝐢𝐨𝐧 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 𝐟𝐨𝐫 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐋𝐚𝐫𝐠𝐞 𝐕𝐢𝐬𝐢𝐨𝐧-𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 𝐭𝐨 𝐂𝐨-𝐏𝐢𝐥𝐨𝐭 𝐄𝐧𝐝𝐨𝐬𝐜𝐨𝐩𝐢𝐜 𝐒𝐮𝐛𝐦𝐮𝐜𝐨𝐬𝐚𝐥 𝐃𝐢𝐬𝐬𝐞𝐜𝐭𝐢𝐨𝐧” has been accepted to 𝐀𝐂𝐌 𝐌𝐌 𝟐𝟎𝟐𝟓 (𝐃𝐚𝐭𝐚𝐬𝐞𝐭 𝐓𝐫𝐚𝐜𝐤)!

We built 𝐂𝐨𝐏𝐄𝐒𝐃 to help AI better understand surgical workflows—especially the complex motions involved in 𝐄𝐧𝐝𝐨𝐬𝐜𝐨𝐩𝐢𝐜 𝐒𝐮𝐛𝐦𝐮𝐜𝐨𝐬𝐚𝐥 𝐃𝐢𝐬𝐬𝐞𝐜𝐭𝐢𝐨𝐧 (𝐄𝐒𝐃). The dataset includes:

📹 35+ hours of annotated surgical videos

🖼️ 17,679 labeled frames

🔍 88,395 motion annotations across multiple levels

We designed a hierarchical annotation scheme to capture fine-grained surgical motions, especially focusing on the submucosal dissection phase. Our goal is to enable 𝐯𝐢𝐬𝐢𝐨𝐧-𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐦𝐨𝐝𝐞𝐥𝐬 that can one day assist surgeons in real-time—like a smart co-pilot in the OR.

Thanks to all collaborators (Guankun Wang, Han Xiao, Huxin Gao, Renrui Zhang, Long Bai, Xiaoxiao Yang, Zhen Li, Hongsheng Li,

Hongliang Ren) and institutions (CUHK, Shanghai AI Lab, Qilu Hospital of SDU) involved. We’re excited to see how this dataset can push forward research in 𝐬𝐮𝐫𝐠𝐢𝐜𝐚𝐥 𝐀𝐈, 𝐫𝐨𝐛𝐨𝐭𝐢𝐜𝐬, and 𝐦𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠.

📄 Check out the paper: https://lnkd.in/gkF6A4QY