Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
Digestive Disease Center, Showa University Northern Yokohama Hospital, Chigasaki-chuo 35-1, Tsuzuki-ku, Yokohama, 224-8503, Japan.
Int J Comput Assist Radiol Surg. 2022 Nov;17(11):2051-2063. doi: 10.1007/s11548-022-02696-y. Epub 2022 Aug 8.
Precise polyp detection and localisation are essential for colonoscopy diagnosis. Statistical machine learning with a large-scale data set can contribute to the construction of a computer-aided diagnosis system for the prevention of overlooking and miss-localisation of a polyp in colonoscopy. We propose new visual explaining methods for a well-trained object detector, which achieves fast and accurate polyp detection with a bounding box towards a precise automated polyp localisation.
We refine gradient-weighted class activation mapping for more accurate highlighting of important patterns in processing a convolutional neural network. Extending the refined mapping into multiscaled processing, we define object activation mapping that highlights important object patterns in an image for a detection task. Finally, we define polyp activation mapping to achieve precise polyp localisation by integrating adaptive local thresholding into object activation mapping. We experimentally evaluate the proposed visual explaining methods with four publicly available databases.
The refined mapping visualises important patterns in each convolutional layer more accurately than the original gradient-weighted class activation mapping. The object activation mapping clearly visualises important patterns in colonoscopic images for polyp detection. The polyp activation mapping localises the detected polyps in ETIS-Larib, CVC-Clinic and Kvasir-SEG database with mean Dice scores of 0.76, 0.72 and 0.72, respectively.
We developed new visual explaining methods for a convolutional neural network by refining and extending gradient-weighted class activation mapping. Experimental results demonstrated the validity of the proposed methods by showing that accurate visualisation of important patterns and localisation of polyps in a colonoscopic image. The proposed visual explaining methods are useful for the interpreting and applying a trained polyp detector.
精确的息肉检测和定位对于结肠镜诊断至关重要。利用大规模数据集进行统计机器学习可以为构建结肠镜中息肉遗漏和定位错误的计算机辅助诊断系统做出贡献。我们提出了一种新的视觉解释方法，用于训练有素的目标检测器，该检测器可以实现快速准确的息肉检测，并通过边界框实现精确的自动息肉定位。
我们对梯度加权类激活映射进行了细化，以更准确地突出处理卷积神经网络中的重要模式。通过将细化映射扩展到多尺度处理，我们定义了对象激活映射，用于突出图像中检测任务的重要对象模式。最后，我们通过将自适应局部阈值集成到对象激活映射中，定义了息肉激活映射，以实现精确的息肉定位。我们使用四个公开可用的数据库对所提出的视觉解释方法进行了实验评估。
细化映射比原始梯度加权类激活映射更准确地可视化每个卷积层中的重要模式。对象激活映射清楚地可视化了结肠镜图像中用于息肉检测的重要模式。息肉激活映射分别在 ETIS-Larib、CVC-Clinic 和 Kvasir-SEG 数据库中定位检测到的息肉，平均 Dice 分数分别为 0.76、0.72 和 0.72。
我们通过细化和扩展梯度加权类激活映射，为卷积神经网络开发了新的视觉解释方法。实验结果表明，通过准确地可视化重要模式和定位结肠镜图像中的息肉，证明了所提出方法的有效性。所提出的视觉解释方法对于解释和应用训练有素的息肉检测器非常有用。
