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Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023; 26th International C Hayit Greenspan,Anant Madabhushi,Russell Tay

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41#
發(fā)表于 2025-3-28 15:33:38 | 只看該作者
0302-9743 e 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023...The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in
42#
發(fā)表于 2025-3-28 19:47:40 | 只看該作者
43#
發(fā)表于 2025-3-29 01:01:41 | 只看該作者
44#
發(fā)表于 2025-3-29 04:08:12 | 只看該作者
45#
發(fā)表于 2025-3-29 09:12:17 | 只看該作者
46#
發(fā)表于 2025-3-29 15:00:22 | 只看該作者
SEDSkill: Surgical Events Driven Method for?Skill Assessment from?Thoracoscopic Surgical Videosills to prevent complications and enhance patient outcomes. Consequently, surgical skill assessment (SKA) for MVR is essential for certifying novice surgeons and training purposes. However, current automatic SKA approaches have inherent limitations that include the absence of public thoracoscopy-ass
47#
發(fā)表于 2025-3-29 16:20:12 | 只看該作者
Neural LerPlane Representations for?Fast 4D Reconstruction of?Deformable Tissueshods relying only on implicit representations are computationally expensive and require dozens of hours, which limits further practical applications. To address this challenge, we introduce LerPlane, a novel method for fast and accurate reconstruction of surgical scenes under a single-viewpoint sett
48#
發(fā)表于 2025-3-29 19:45:15 | 只看該作者
SegmentOR: Obtaining Efficient Operating Room Semantics Through Temporal Propagationarning methods for operating room recognition tasks still require substantial quantities of annotated data. In this paper, we introduce a method for weakly-supervised semantic segmentation for surgical operating rooms. Our method operates directly on 4D point cloud sequences from multiple ceiling-mo
49#
發(fā)表于 2025-3-30 00:26:01 | 只看該作者
Revisiting Distillation for Continual Learning on Visual Question Localized-Answering in Robotic Surswers, the VQLA system can highlight the interested region for better surgical scene understanding. However, deep neural networks (DNNs) suffer from catastrophic forgetting when learning new knowledge. Specifically, when DNNs learn on incremental classes or tasks, their performance on old tasks drop
50#
發(fā)表于 2025-3-30 06:11:51 | 只看該作者
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