通过模拟白光反光增强数据，提升深度学习结肠息肉检测准确性
White Light Specular Reflection Data Augmentation for Deep Learning Polyp Detection
- 构建人工光源库并用滑动窗口注入训练图像
- 使模型在更多误判场景中学习，减少假阳性
- 适合医学影像、深度学习鲁棒性研究者参考
结直肠癌是当前最致命的癌症之一，但通过结肠镜早期发现恶性息肉可有效预防。尽管该方法已拯救无数生命，人为漏检仍是重大挑战，可能危及患者性命。深度学习息肉检测器提供了有前景的解决方案，但现有模型常将内窥镜白光反射误判为息肉，导致假阳性。为此，本文提出一种新型数据增强方法，通过人工添加更多白光反射以制造更难的训练场景。具体而言，首先利用训练集生成人工光源库；然后识别出不应添加光源的区域；最后采用滑动窗口方法将人工光源注入符合条件的图像区域，生成增强图像。通过给予模型更多犯错机会，我们假设其也将获得更多纠错学习机会，从而提升检测性能。实验结果证明了该方法的有效性。
原文摘要 · Abstract (English)
Colorectal cancer is one of the deadliest cancers today, but it can be prevented through early detection of malignant polyps in the colon, primarily via colonoscopies. While this method has saved many lives, human error remains a significant challenge, as missing a polyp could have fatal consequences for the patient. Deep learning (DL) polyp detectors offer a promising solution. However, existing DL polyp detectors often mistake white light reflections from the endoscope for polyps, which can lead to false positives.To address this challenge, in this paper, we propose a novel data augmentation approach that artificially adds more white light reflections to create harder training scenarios. Specifically, we first generate a bank of artificial lights using the training dataset. Then we find the regions of the training images that we should not add these artificial lights on. Finally, we propose a sliding window method to add the artificial light to the areas that fit of the training images, resulting in augmented images. By providing the model with more opportunities to make mistakes, we hypothesize that it will also have more chances to learn from those mistakes, ultimately improving its performance in polyp detection. Experimental results demonstrate the effectiveness of our new data augmentation method.
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