mlsanityData to 14 Oct 2026

Redefining cystoscopy with ai: bladder cancer diagnosis using an efficient hybrid cnn-transformer model

arXiv 2403.03879 ↗First posted 6 Mar 2024cs.CV cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 6 Mar 2024.

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References · 17

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5 more
  1. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 959
  2. 2018BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentationcited by 645
  3. 2022Transformer Language Models without Positional Encodings Still Learn Positional Informationcited by 150
  4. 2019DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentationcited by 100
  5. 2022Semi-supervised Bladder Tissue Classification in Multi-Domain Endoscopic Imagescited by 4

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