Labeled Images for Ulcerative Colitis (LIMUC) Dataset

The LUMIC dataset compromises 11276 images from 564 patients and 1043 colonoscopy procedures, who underwent colonoscopy for ulcerative colitis between December 2011 and July 2019 at the Department of Gastroenterology in Marmara University School of Medicine. Two experienced gastroenterologists blindly reviewed and classified all images according to the endoscopic Mayo score (EMS). Images that were differently labeled by two reviewers were also labeled by a third experienced reviewer independently without seeing their previous labels. The final EMS for differently labeled images was determined using majority voting.

Suggested Metrics

Since there are imbalances and ordinality among classes (Mayo-0, Mayo-1, Mayo-2, Mayo-3), quadratic weighted kappa (QWK) can be used as the main performance metric. The QWK is one of the commonly used statistics for the assessment of agreement on an ordinal scale and it is one of the best singular performance metrics for this problem regarding class imbalances. Mean absolute error (MAE), macro F1 score, or macro accuracy can be used as alternative performance metrics.

LIMUC Code Repository

Many scripts for preprocessing, splitting, training, and validating the dataset are provided in this GitHub repository.

Terms and Conditions

In all documents and publications that use the LIMUC dataset or report experimental results based on the LIMUC dataset, citation should be included.

BreathBase: Intra-Speech Breathing Dataset

BreathBase contains 5070 breath instances detected on the recordings of 20 participants reading pre-prepared random pseudo texts in 5 different postures with 4 different microphones, simultaneously.

It is recorded in a studio with a maximum background noise of 40 dB SPL and with professional recording equipment. It also provides tagging for 5 different postures and 4 different channels as different recording conditions for data variety.

More than 90% of the recordings is shorter than 600 milliseconds. The minimum number of breath instances per participant is 89, the maximum number of instances is 710 and the average for all participants is 253.5 breath instances

Camera Sabotage / Camera Tamper Detection Dataset

Citation Information:
A.Saglam, A.Temizel, “Real-time Adaptive Camera Tamper Detection for Video Surveillance”, in Proceedings IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Sept. 2009.

Crowd Behaviour Analysis Dataset

Citation Information:
C. Ongun, A.Temizel, T.Taskaya Temizel, “Local Anomaly Detection in Crowded Scenes Using Finite-Time Lyapunov Exponent Based Clustering”, IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Aug. 2014.