Brzeski Adam, Dziubich Tomasz, Krawczyk Henryk
Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, 80-233 Gdańsk, Poland.
Sensors (Basel). 2023 Dec 8;23(24):9717. doi: 10.3390/s23249717.
The presented paper investigates the problem of endoscopic bleeding detection in endoscopic videos in the form of a binary image classification task. A set of definitions of high-level visual features of endoscopic bleeding is introduced, which incorporates domain knowledge from the field. The high-level features are coupled with respective feature descriptors, enabling automatic capture of the features using image processing methods. Each of the proposed feature descriptors outputs a feature activation map in the form of a grayscale image. Acquired feature maps can be appended in a straightforward way to the original color channels of the input image and passed to the input of a convolutional neural network during the training and inference steps. An experimental evaluation is conducted to compare the classification ROC AUC of feature-extended convolutional neural network models with baseline models using regular color image inputs. The advantage of feature-extended models is demonstrated for the Resnet and VGG convolutional neural network architectures.
本文以二值图像分类任务的形式研究了内镜视频中的内镜出血检测问题。引入了一组内镜出血的高级视觉特征定义，其中纳入了该领域的专业知识。高级特征与各自的特征描述符相结合，从而能够使用图像处理方法自动捕获这些特征。每个提出的特征描述符都会输出一个灰度图像形式的特征激活图。在训练和推理步骤中，获取的特征图可以直接附加到输入图像的原始颜色通道上，并传递到卷积神经网络的输入端。进行了一项实验评估，以比较使用常规彩色图像输入的基线模型与特征扩展卷积神经网络模型的分类ROC AUC。结果表明，对于Resnet和VGG卷积神经网络架构，特征扩展模型具有优势。
