This paper presents a novel supervised convolutional neural network
architecture, "DUCK-Net", capable of effectively learning and generalizing from
small amounts of medical images to perform accurate segmentation tasks. Our
model utilizes an encoder-decoder structure with a residual downsampling
mechanism and a custom convolutional block to capture and process image
information at multiple resolutions in the encoder segment. We employ data
augmentation techniques to enrich the training set, thus increasing our model's
performance. While our architecture is versatile and applicable to various
segmentation tasks, in this study, we demonstrate its capabilities specifically
for polyp segmentation in colonoscopy images. We evaluate the performance of
our method on several popular benchmark datasets for polyp segmentation,
Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, and ETIS-LARIBPOLYPDB showing that it
achieves state-of-the-art results in terms of mean Dice coefficient, Jaccard
index, Precision, Recall, and Accuracy. Our approach demonstrates strong
generalization capabilities, achieving excellent performance even with limited
training data. The code is publicly available on GitHub:
this https URL

我们提出了一种新型的监督卷积神经网络架构 “DUCK-Net”，能够从少量的医学图像中有效学习和概括，以执行准确的分割任务。我们的模型利用编码器 - 解码器结构、残差下采样机制和自定义卷积块，以在编码器段的多个分辨率上捕获和处理图像信息。我们采用数据增强技术丰富训练集，从而提高模型性能。虽然我们的架构多用途，并适用于各种分割任务，但在这项研究中，我们特别展示了其在结肠镜图像的息肉分割方面的能力。我们在几个流行的息肉分割基准数据集 Kvasir-SEG、CVC-ClinicDB、CVC-ColonDB 和 ETIS-LARIBPOLYPDB 上评估了方法的性能，结果表明在 Dice 系数均值、Jaccard 指数、精确度、召回率和准确度等方面取得了最先进的结果。我们的方法展示了强大的泛化能力，即使在有限的训练数据下也能实现出色的性能。代码已公开在 GitHub 上：this https URL

使用 DUCK-Net 进行息肉图像分割

Using DUCK-Net for Polyp Image Segmentation

In this paper we describe the problem of painter classification, and propose
a novel approach based on deep convolutional autoencoder neural networks. While
previous approaches relied on image processing and manual feature extraction
from paintings, our approach operates on the raw pixel level, without any
preprocessing or manual feature extraction. We first train a deep convolutional
autoencoder on a dataset of paintings, and subsequently use it to initialize a
supervised convolutional neural network for the classification phase.
The proposed approach substantially outperforms previous methods, improving
the previous state-of-the-art for the 3-painter classification problem from
90.44% accuracy (previous state-of-the-art) to 96.52% accuracy, i.e., a 63%
reduction in error rate.

本文提出一种基于深度卷积自编码神经网络的新方法，用于画家分类问题，相对于先前的图像处理和手动特征提取，该方法在原始像素级别进行操作，采用深度卷积自编码和有监督的卷积神经网络，将原有的 90.44％分类准确率提高到 96.52％。