Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020; 23rd International C Anne L. Martel,Purang Abolmaesumi,Leo Joskow

琺瑯

Improved Resection Margins in Surgical Oncology Using Intraoperative Mass Spectrometry, respectively. An attention-based MIL model was adapted and applied to this dataset. RESULTS: Our models were able to predict BCC at surgical margins with AUC as high as 91%. The models were robust to changes in cautery tip but their performance decreased slightly. The models were also tested intra

LARK

An Interactive Mixed Reality Platform for Bedside Surgical Proceduresprecision of the insertions with mixed reality as well as the usability of our navigation system. The results indicate that using mixed reality improves the accuracy by over 35% and that the system ranks high based on the usability score.

Radiation

宮殿般

FER

A New Electromagnetic-Video Endoscope Tracking Method via Anatomical Constraints and Historically Obical data, with the experimental results showing that our proposed method fully outperforms current hybrid approaches. In particular, the tracking error was significantly reduced from (5.9?mm, 9.9.) to (3.3?mm, 8.6.).

Pandemic

LAST

Abrupt

Repatriate

Reinforcement Learning of Musculoskeletal Control from Functional Simulationsontrol is introduced, enabling straightforward extension to additional muscles and higher degrees of freedom. Using the biomechanical model, multiple episodes are simulated on a cluster simultaneously using the evolving neural models of the DRL being trained. Results are presented for a single-axis

信任

MvMM-RegNet: A New Image Registration Framework Based on Multivariate Mixture Model and Neural Netwof implementing groupwise registration. We highlight the versatility of the proposed framework for various applications on multimodal cardiac images, including single-atlas-based segmentation (SAS) via pairwise registration and multi-atlas segmentation (MAS) unified by groupwise registration. We eval