In this paper, we present our approach to the Auto WCEBleedGen Challenge V2
2024. Our solution combines the Swin Transformer for the initial classification
of bleeding frames and RT-DETR for further detection of bleeding in Wireless
Capsule Endoscopy (WCE), enhanced by a series of image preprocessing steps.
These steps include converting images to Lab colour space, applying Contrast
Limited Adaptive Histogram Equalization (CLAHE) for better contrast, and using
Gaussian blur to suppress artefacts. The Swin Transformer utilizes a tiered
architecture with shifted windows to efficiently manage self-attention
calculations, focusing on local windows while enabling cross-window
interactions. RT-DETR features an efficient hybrid encoder for fast processing
of multi-scale features and an uncertainty-minimal query selection for enhanced
accuracy. The class activation maps by Ablation-CAM are plausible to the
model's decisions. On the validation set, this approach achieves a
classification accuracy of 98.5% (best among the other state-of-the-art models)
compared to 91.7% without any pre-processing and an $\text{AP}_{50}$ of 66.7%
compared to 65.0% with state-of-the-art YOLOv8. On the test set, this approach
achieves a classification accuracy and F1 score of 87.0% and 89.0%
respectively.

本文描述了我们在 Auto WCEBleedGen 挑战赛 V2 2024 中的方法，结合了 Swin Transformer 用于出血帧的初步分类以及 RT-DETR 用于进一步检测无线胶囊内窥镜图像中的出血，同时还采用了一系列图像预处理步骤，包括将图像转换为 Lab 颜色空间，使用限制对比适应性直方图均衡化（CLAHE）进行对比度增强，以及使用高斯模糊抑制伪影。该方法在验证集上达到了 98.5％的分类准确率（优于其他最先进模型的表现），相较于无任何预处理的 91.7％，在 $	ext {AP}_{50}$ 上达到了 66.7％（相较于最先进的 YOLOv8 的 65.0％）。在测试集上，该方法的分类准确性和 F1 得分分别为 87.0％和 89.0％。