Summary of Early Explorations Of Lightweight Models For Wound Segmentation on Mobile Devices, by Vanessa Borst et al.
Early Explorations of Lightweight Models for Wound Segmentation on Mobile Devices
by Vanessa Borst, Timo Dittus, Konstantin Müller, Samuel Kounev
First submitted to arxiv on: 10 Jul 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 The paper addresses the need for computer-aided wound recognition in healthcare, leveraging smartphone photos to enable objective and convenient therapy monitoring. It proposes three lightweight architectures for mobile wound segmentation, using public datasets and a UNet baseline. The results show promising performance from ENet, TopFormer, and a larger UNeXt variant, comparable to the UNet baseline. A smartphone app deployment demonstrates the effectiveness of TopFormer in distinguishing wounds from objects. Future work aims to improve mask contours. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a better way to look at old people’s wounds using photos taken on phones. Right now, doctors and nurses have to look at these photos and decide what kind of wound it is, which isn’t very good because it can be tricky. This makes it hard for older people to get the right treatment from home. The paper looks at three new ways to make this process better using special models that work on phones. The results are pretty good, and one model does a great job of telling apart real wounds from objects that look like wounds. More work is needed to make it even better. |
Keywords
» Artificial intelligence » Mask » Unet
