  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:
https://github.com/RazvanDu/DUCK-Net
