Summary of Modular Deep Active Learning Framework For Image Annotation: a Technical Report For the Ophthalmo-ai Project, by Md Abdul Kadir et al.
Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project
by Md Abdul Kadir, Hasan Md Tusfiqur Alam, Pascale Maul, Hans-Jürgen Profitlich, Moritz Wolf, Daniel Sonntag
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel deep learning-based framework, MedDeepCycleAL, is introduced for automating medical image segmentation and annotation. This end-to-end framework incorporates Active Learning methods to minimize the need for ground truth data, while allowing researchers to choose from various deep learning models. The framework includes a user-friendly annotation tool that supports classification and segmentation of medical images, without requiring programming experience. The framework’s flexibility and ease of use make it an attractive solution for researchers in medical imaging and disease diagnosis. By accurately segmenting medical images, MedDeepCycleAL can significantly reduce the time and effort required for manual annotation, ultimately improving patient care. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MedDeepCycleAL is a new way to help doctors and researchers work with medical images. Right now, they have to spend a lot of time labeling these images by hand, which is very tedious. This framework uses deep learning computers to automate this process. It also helps them use less data to train the computer models, making it easier to get started. The best part is that anyone can use it without needing to know how to program. In this project, MedDeepCycleAL was tested on ophthalmology images, but it could be used for any type of medical image. |
Keywords
» Artificial intelligence » Active learning » Classification » Deep learning » Image segmentation
