The TUM LapChole dataset for the M2CAI 2016 workflow challenge

10/28/2016
by   Ralf Stauder, et al.
0

In this technical report we present our collected dataset of laparoscopic cholecystectomies (LapChole). Laparoscopic videos of a total of 20 surgeries were recorded and annotated with surgical phase labels, of which 15 were randomly pre-determined as training data, while the remaining 5 videos are selected as test data. This dataset was later included as part of the M2CAI 2016 workflow detection challenge during MICCAI 2016 in Athens.

READ FULL TEXT

page 2

page 4

research
03/24/2021

MIcro-Surgical Anastomose Workflow recognition challenge report

The "MIcro-Surgical Anastomose Workflow recognition on training sessions...
research
10/27/2016

Single- and Multi-Task Architectures for Surgical Workflow Challenge at M2CAI 2016

The surgical workflow challenge at M2CAI 2016 consists of identifying 8 ...
research
08/29/2021

The rUNSWift SPL Field Segmentation Dataset

In RoboCup SPL, soccer field segmentation has been widely recognised as ...
research
09/30/2021

Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark

PURPOSE: Surgical workflow and skill analysis are key technologies for t...
research
10/18/2016

M2CAI Workflow Challenge: Convolutional Neural Networks with Time Smoothing and Hidden Markov Model for Video Frames Classification

Our approach is among the three best to tackle the M2CAI Workflow challe...
research
10/19/2019

The Deepfake Detection Challenge (DFDC) Preview Dataset

In this paper, we introduce a preview of the Deepfakes Detection Challen...
research
02/11/2022

PEg TRAnsfer Workflow recognition challenge report: Does multi-modal data improve recognition?

This paper presents the design and results of the "PEg TRAnsfert Workflo...

Please sign up or login with your details

Forgot password? Click here to reset