Auto-WCEBleedGen Challenge Version V2

Automatic Classification between Bleeding and Non-Bleeding frames and further Detection and Segmentation of the Bleeding Region



Gastrointestinal (GI) bleeding is a medical condition characterized by bleeding in the GI tract, which circumscribes oesophagus, stomach, small intestine, large intestine (colon), rectum, and anus. Since blood flows into the GI tract, a cascade of risks emerges, ranging from immediate dangers to potential long-term consequences. Excessive blood loss from GI bleeding may lead to a drop in blood pressure, reduced oxygen delivery to organs and tissues, and potentially life-threatening organ dysfunction.
According to World Health Organization (WHO), GI bleeding is responsible for approximately 300,000 deaths every year globally. These statistics serve as a catalyst for research, propelling innovative treatment modalities and diagnostic advancements aimed at mitigating the dangers posed by GI bleeding. In last decade, the availability of advanced diagnostic innovations like Wireless Capsule Endoscopy (WCE) has led to better understanding of the GI bleeding in GI tract. The disposable capsule-shaped device travels inside the GI tract via peristalsis and comprises of an optical dome, a battery, an illuminator, an imaging sensor, and a radio-frequency transmitter. During 8-12 hours of WCE procedure, a video of the GI tract trajectory is recorded on a device attached to the patient’s belt which produces about 57,000-1,00,000 frames; analysed posterior by experienced gastroenterologists.
Presently, an experienced gastroenterologist takes approximately 2−3 hours to inspect the captured video of one-patient through a frame-by-frame analysis which is not only time-consuming but also susceptible to human error. In view of the poor ratio of patient-to-doctor across globe, there arises a need for investigation and state-of-the-art development of robust, interpretable and generalized Artificial Intelligence (AI) models. This will aid in reducing the burden on gastroenterologists and save their valuable time by computer-aided classification between bleeding and non-bleeding frames and further detection and segmentation of bleeding region in that frame.
Auto-WCEBleedGen Challenge Version V1 was a huge success with +1200 participation across globe. It was organized virtually by MISAHUB (Medical Imaging and Signal Analysis Hub), in collaboration with the 8th International Conference on CVIP (Computer Vision and Image Processing 2023), IIT Jammu, India from August 15 – October 14, 2023. It focused on automatic detection and classification of bleeding and non-bleeding frames in WCE.
Following its success, we bring to you, Auto-WCEBleedGen Challenge Version V2 which focuses on automatic classification of bleeding and non-bleeding frames and further detection and segmentation of bleeding region in that frame. We have updated the annotations of the multiple bleeding sites present in the training dataset (WCEBleedGen). We have also updated the annotations and class labels of the testing dataset (Auto-WCEBleedGen Test) and provided un-marked images of dataset 1.



Events Dates
Launch of the challenge January 20, 2024
E-Registration January 20 – February 10, 2024
Release of Training Dataset January 20, 2024
Release of Testing Dataset February 11, 2024
Result submission February 22 – February 24, 2024
Announcement of top three winning teams March 06, 2024
Paper Submission (optional) April 03, 2024
Presentation by the winning team (1st position only) – ICIP 2024 October 27 – October 30, 2024

Registration and Rules

Rules for Participation:

Rules for Team Formation:

Rules for use of Training Dataset:

Rules for use of Testing Dataset:

Submission Format:

Each team is required to submit the results via EMAIL to with following rules in mind.

Important Notes:

Criteria of judging a submission:

Datasets To Be Used

Training dataset: WCEbleedGen Version V2

The training dataset consists of 2618 bleeding and non-bleeding WCE frames collected from multiple internet resources, datasets with a vast variety and types of GI bleeding throughout the GI tract along with medically validated binary masks and bounding boxes in three formats (txt, XML and YOLO txt). The Version V1 of this dataset was utilized as a training dataset in Auto-WCEBleedGen Challenge Version V1.
After the challenge, a Version V2 was released on Nov 19, 2023. In this version, the multiple bleeding frames present in Version V1 were re-annotated. Their new XML and YOLO-TXT were also added. This dataset is first-of-its-kind and promotes generalized comparison with existing state-of-the-art methods, and aims to contribute to better interpretability, and reproducibility of such automated systems.

Testing dataset: AutoWCEBleedGen-Test Dataset Version V2

AutoWCEBleedGen-Test dataset is an independently collected WCE data containing bleeding and non-bleeding frames of 30 patients suffering from acute, chronic and occult GI bleeding referred at Department of Gastroenterology and HNU, All India Institute of Medical Sciences (AIIMS), New Delhi, India. It was utilized as a testing dataset in the AutoWCEBleedGen Challenge Version V1. It was only accessible to challenge participants and shared through Google drive link throughout the challenge.
It consists of a total 564 frames. It is divided into dataset 1 and 2. Dataset 1 contains 49 frames which were randomly collected from seven different patient's data at AIIMS. The frames were then annotated by a group of experienced gastroenterologists at AIIMS. The annotations were marked in the frames. The dataset 2 contains 515 frames which were collected from twenty-three different patient's data. The annotations were NOT marked in the frames. A list of image names with respect to patient were released for the challenge participants. Dataset 1 was developed for non-sequential, random frame analysis. Dataset 2 was developed for sequential-frame analysis.
After the challenge, we developed the improved version of the test dataset and released it Nov 14, 2023 at zenedo platform. In the improved version, we have updated the annotations (binary masks) of dataset 1 and 2, validated with the team of gastroenterologists at AIIMS, and provided un-marked images of dataset 1. Testing dataset of AutoWCEBleedGen Challenge Version V1 was also released in this.
For AutoWCEBleedGen Challenge Version V2, we will release the version V2 of the AutoWCEBleedGen-Test Dataset on February 11, 2024. This version will include medically annotated bounding boxes in three different formats (txt, XML and YOLO txt).



S.No. Position Team Name Affiliation
1 First Position ColonNet Indian Institute of Information Technology, Ranchi
2 Second Position ACVLab Institute of Data Science, National Cheng Kung University, Taiwan
3 Third Position Failed Wizards Indian Institute of Technology, Tirupati



Prof. Nidhi Goel

Dept. of ECE


Palak Handa

Dept. of ECE

DTU, Delhi

Dr. Deepak Gunjan

Dept. of Gastroenterology and HNU



Deepti Chhabra


(Website Management)

Anushka Saini


(Registrations, E-mail and Certificates)

Advika Thakur


(Social Media)

Divyansh Nautiyal

GGSIPU, New Delhi

(Dataset Development, Evaluation)

Manas Dhir

GGSIPU, New Delhi


Nishu Pandey