Summary of Redefining Cystoscopy with Ai: Bladder Cancer Diagnosis Using An Efficient Hybrid Cnn-transformer Model, by Meryem Amaouche and Ouassim Karrakchou and Mounir Ghogho and Anouar El Ghazzaly and Mohamed Alami and Ahmed Ameur
Redefining cystoscopy with ai: bladder cancer diagnosis using an efficient hybrid cnn-transformer model
by Meryem Amaouche, Ouassim Karrakchou, Mounir Ghogho, Anouar El Ghazzaly, Mohamed Alami, Ahmed Ameur
First submitted to arxiv on: 6 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 This research proposes a deep learning approach to detect and segment bladder cancer using computer vision techniques. The method combines convolutional neural networks (CNNs) with a lightweight transformer and attention gates, which enhance features by fusing self and spatial attention. This architecture is efficient, making it suitable for real-time inference in medical scenarios. Experiments show that the model balances computational efficiency and diagnostic accuracy, rivaling larger models in performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bladder cancer is a common and costly disease that requires lifelong treatment. Doctors use cystoscopy to diagnose it, but many cases are misdiagnosed or undiagnosed. This study develops a new way to detect and segment bladder cancer using deep learning. The method uses special computer programs called CNNs and attention gates to look at pictures of the bladder and identify signs of cancer. This approach is fast and accurate, making it useful for doctors who need to make quick decisions. |
Keywords
* Artificial intelligence * Attention * Deep learning * Inference * Transformer
