Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis

Jin, Yueming; Li, Huaxia; Dou, Qi; Chen, Hao; Qin, Jing; Heng, Pheng‐Ann

doi:10.48550/arxiv.1907.06099

Search citation statements

Order By: Relevance

Paper Sections

Select...

Methods1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Convolutional architectures such as ResNet-50 have been extensively used for phase segmentation of endoscopic videos. They serve as feature extraction backbones for many state-of-the-art recognition architectures [15,7,33]. Our baseline consists of a ResNet-50 pre-trained on ImageNet without temporal modeling.…”

Section: Methodsmentioning

confidence: 99%

Know your sensORs -- A Modality Study For Surgical Action Classification

Bastian¹,

Czempiel²,

Heiliger³

et al. 2022

Preprint

View full text Add to dashboard Cite

The surgical operating room (OR) presents many opportunities for automation and optimization. Videos from various sources in the OR are becoming increasingly available. The medical community seeks to leverage this wealth of data to develop automated methods to advance interventional care, lower costs, and improve overall patient outcomes. Existing datasets from OR room cameras are thus far limited in size or modalities acquired, leaving it unclear which sensor modalities are best suited for tasks such as recognizing surgical action from videos. This study demonstrates that surgical action recognition performance can vary depending on the image modalities used. We perform a methodical analysis on several commonly available sensor modalities, presenting two fusion approaches that improve classification performance. The analyses are carried out on a set of multi-view RGB-D video recordings of 18 laparoscopic procedures.

show abstract

Section: Methodsmentioning

confidence: 99%