Authors: Meryem Amaouche, Ouassim Karrakchou, Mounir Ghogho, Anouar El Ghazzaly, Mohamed Alami, Ahmed Ameur

Published on: March 06, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2403.03879

Summary

• What is new: A new deep learning approach for bladder cancer detection that combines CNNs with a lightweight transformer and dual attention gates, specifically designed for real-time medical inference.
• Why this is important: Bladder cancer is hard to diagnose accurately due to the reliance on cystoscopy, leading to many cases being undiagnosed or misdiagnosed.
• What the research proposes: A deep learning model that uses CNNs, a lightweight transformer, and attention mechanisms to improve the accuracy and efficiency of bladder cancer detection.
• Results: The model achieves a balance between computational efficiency and diagnostic accuracy, performing comparably to larger models while being suitable for real-time use.

Technical Details

Technological frameworks used: Combination of Convolutional Neural Networks (CNNs) and Transformers

Models used: Positional-encoding-free transformer with dual attention gates

Data used: Cystoscopic imaging

Potential Impact

Healthcare providers, medical imaging companies, and AI healthcare startups could benefit or be disrupted by the adoption of this technology.

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