Redefining cystoscopy with ai: bladder cancer diagnosis using an efficient hybrid cnn-transformer model
This work addresses the problem of misdiagnosis and high costs in bladder cancer treatment by providing a more accurate and efficient AI tool for doctors, though it is incremental as it builds on existing deep learning methods.
The paper tackled bladder cancer diagnosis via cystoscopy by proposing an efficient hybrid CNN-transformer model with dual attention gates, achieving performance rivaling larger models while maintaining computational efficiency suitable for real-time medical use.
Bladder cancer ranks within the top 10 most diagnosed cancers worldwide and is among the most expensive cancers to treat due to the high recurrence rates which require lifetime follow-ups. The primary tool for diagnosis is cystoscopy, which heavily relies on doctors' expertise and interpretation. Therefore, annually, numerous cases are either undiagnosed or misdiagnosed and treated as urinary infections. To address this, we suggest a deep learning approach for bladder cancer detection and segmentation which combines CNNs with a lightweight positional-encoding-free transformer and dual attention gates that fuse self and spatial attention for feature enhancement. The architecture suggested in this paper is efficient making it suitable for medical scenarios that require real time inference. Experiments have proven that this model addresses the critical need for a balance between computational efficiency and diagnostic accuracy in cystoscopic imaging as despite its small size it rivals large models in performance.