用Transformer模型自动识别胶囊内镜中的出血区域并分类，准确率达98%以上。
Transformer-Based Wireless Capsule Endoscopy Bleeding Tissue Detection and Classification
- 基于DETR架构，结合ResNet50与Transformer编码器解码器进行端到端检测
- 在验证集上分类准确率98.28%，检测mAP达0.7328，获挑战赛第三名
- 代码开源，适合医疗图像分析与AI辅助诊断研究者参考
受Transformer在计算机视觉任务中成功应用的启发，我们设计了一个端到端可训练模型，用于自动检测和分类从无线胶囊内镜（WCE）视频中提取的出血与非出血帧。该模型基于DETR架构，采用ResNet50进行特征提取，利用Transformer编码器-解码器检测出血区域，并通过前馈神经网络完成分类。在Auto-WCEBleedGen Version 1挑战赛训练集上以端到端方式训练，模型可统一完成检测与分类任务。在Auto-WCEBleedGen Version 1验证集上，分类准确率、召回率和F1分数分别为98.28%、96.79%和98.37%；检测任务的平均精度（AP @ 0.5）和均值平均精度（mAP）分别为0.7447和0.7328。该成果在挑战赛中获得第三名。代码已公开：https://github.com/BasitAlawode/WCEBleedGen。
原文摘要 · Abstract (English)
Informed by the success of the transformer model in various computer vision tasks, we design an end-to-end trainable model for the automatic detection and classification of bleeding and non-bleeding frames extracted from Wireless Capsule Endoscopy (WCE) videos. Based on the DETR model, our model uses the Resnet50 for feature extraction, the transformer encoder-decoder for bleeding and non-bleeding region detection, and a feedforward neural network for classification. Trained in an end-to-end approach on the Auto-WCEBleedGen Version 1 challenge training set, our model performs both detection and classification tasks as a single unit. Our model achieves an accuracy, recall, and F1-score classification percentage score of 98.28, 96.79, and 98.37 respectively, on the Auto-WCEBleedGen version 1 validation set. Further, we record an average precision (AP @ 0.5), mean-average precision (mAP) of 0.7447 and 0.7328 detection results. This earned us a 3rd place position in the challenge. Our code is publicly available via https://github.com/BasitAlawode/WCEBleedGen.
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