颜色校正对胶囊内镜病理检测效果影响复杂，未显著提升模型性能。
Influence of color correction on pathology detection in Capsule Endoscopy
- 对比原始与校正后图像，使用Retinanet和YOLOv5检测病理
- 校正使检测框变大，交并比提升但假阳性增多，整体指标无明显改善
- 结果提醒医疗影像分析需谨慎处理颜色干扰，适合临床算法开发者参考
近年来，深度学习在无线胶囊内镜（WCE）病理检测中得到应用。然而，训练数据的颜色质量会影响模型在检测、分割和分类任务中的表现。本文评估了两种主流目标检测模型——Retinanet与YOLOv5——在原始及经两种不同颜色校正函数处理的SEE-AI数据集上的表现。结果显示，颜色校正导致模型生成更大的检测框和更高的与真实标注的交并比，但同时增加了特定病灶的假阳性数量。这些变化并未带来F1分数、IoU和AP50等指标的一致性提升。代码已公开于https://github.com/agossouema2011/WCE2024。
原文摘要 · Abstract (English)
Pathology detection in Wireless Capsule Endoscopy (WCE) using deep learning has been explored in the recent past. However, deep learning models can be influenced by the color quality of the dataset used to train them, impacting detection, segmentation and classification tasks. In this work, we evaluate the impact of color correction on pathology detection using two prominent object detection models: Retinanet and YOLOv5. We first generate two color corrected versions of a popular WCE dataset (i.e., SEE-AI dataset) using two different color correction functions. We then evaluate the performance of the Retinanet and YOLOv5 on the original and color corrected versions of the dataset. The results reveal that color correction makes the models generate larger bounding boxes and larger intersection areas with the ground truth annotations. Furthermore, color correction leads to an increased number of false positives for certain pathologies. However, these effects do not translate into a consistent improvement in performance metrics such as F1-scores, IoU, and AP50. The code is available at https://github.com/agossouema2011/WCE2024. Keywords: Wireless Capsule Endoscopy, Color correction, Retinanet, YOLOv5, Detection
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。