Summary of Wcebleedgen: a Wireless Capsule Endoscopy Dataset and Its Benchmarking For Automatic Bleeding Classification, Detection, and Segmentation, by Palak Handa et al.
WCEbleedGen: A wireless capsule endoscopy dataset and its benchmarking for automatic bleeding classification, detection, and segmentation
by Palak Handa, Manas Dhir, Amirreza Mahbod, Florian Schwarzhans, Ramona Woitek, Nidhi Goel, Deepak Gunjan
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents the development of a medically annotated Wireless Capsule Endoscopy (WCE) dataset called WCEbleedGen, which consists of 2,618 frames for automatic classification, detection, and segmentation of bleeding and non-bleeding frames. The dataset is class-balanced and contains single and multiple bleeding sites. A comprehensive evaluation was conducted using nine classification-based, three detection-based, and three segmentation-based deep learning models, with the best performing models being VGG 19, YOLOv8n, and Linknet for automatic classification, detection, and segmentation, respectively. The dataset is publicly available and aims to aid in developing real-time, multi-task learning-based solutions for automatic bleeding diagnosis in WCE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special kind of medical image dataset called WCEbleedGen. It has lots of pictures taken from inside the body using a tiny camera on a capsule that you swallow. The pictures are labeled as either showing bleeding or not, and there are many different kinds of bleeding sites. Scientists tested some computer programs to see how well they could analyze these images. The best ones were VGG 19, YOLOv8n, and Linknet. This dataset can help doctors develop new ways to quickly diagnose bleeding problems from these camera pictures. |
Keywords
» Artificial intelligence » Classification » Deep learning » Multi task
