用多任务深度学习提升膀胱癌诊断准确率，支持实时分析与在线使用。
Bladder Cancer Diagnosis with Deep Learning: A Multi-Task Framework and Online Platform
- 构建分类、分割与分子分型三任务联合模型，融合注意力机制增强识别能力。
- 分类准确率达93.28%，分割Dice系数达0.9091，性能优于传统方法。
- 推出可在线使用的交互平台，适合临床医生快速部署与辅助诊断。
当前膀胱癌诊断依赖内窥镜检查，高度依赖医生经验，存在主观性和结果差异。本文提出一种面向膀胱癌诊断的多任务深度学习框架，集成基于EfficientNet-B0+CBAM的分类模型、ResNet34-UNet++带自注意力与注意力门控的分割模型，以及基于ConvNeXt-Tiny的分子亚型分类器（用于HER-2、Ki-67标记物）。同时开发基于Gradio的在线平台，支持多格式图像上传、中英双语界面和动态阈值调节。实验表明，分类任务准确率93.28%、F1-score 82.05%、AUC 96.41%，分割任务Dice系数达0.9091。平台显著提升诊断准确性、效率与可及性，代码已开源。该框架推动智能膀胱癌诊断发展，支持早期肿瘤检测与实时反馈，助力泌尿科人工智能辅助决策。
原文摘要 · Abstract (English)
Clinical cystoscopy, the current standard for bladder cancer diagnosis, suffers from significant reliance on physician expertise, leading to variability and subjectivity in diagnostic outcomes. There is an urgent need for objective, accurate, and efficient computational approaches to improve bladder cancer diagnostics. Leveraging recent advancements in deep learning, this study proposes an integrated multi-task deep learning framework specifically designed for bladder cancer diagnosis from cystoscopic images. Our framework includes a robust classification model using EfficientNet-B0 enhanced with Convolutional Block Attention Module (CBAM), an advanced segmentation model based on ResNet34-UNet++ architecture with self-attention mechanisms and attention gating, and molecular subtyping using ConvNeXt-Tiny to classify molecular markers such as HER-2 and Ki-67. Additionally, we introduce a Gradio-based online diagnostic platform integrating all developed models, providing intuitive features including multi-format image uploads, bilingual interfaces, and dynamic threshold adjustments. Extensive experimentation demonstrates the effectiveness of our methods, achieving outstanding accuracy (93.28%), F1-score (82.05%), and AUC (96.41%) for classification tasks, and exceptional segmentation performance indicated by a Dice coefficient of 0.9091. The online platform significantly improved the accuracy, efficiency, and accessibility of clinical bladder cancer diagnostics, enabling practical and user-friendly deployment. The code is publicly available. Our multi-task framework and integrated online tool collectively advance the field of intelligent bladder cancer diagnosis by improving clinical reliability, supporting early tumor detection, and enabling real-time diagnostic feedback. These contributions mark a significant step toward AI-assisted decision-making in urology.
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