Abstract
Colorectal cancer contributes significantly to cancer-related mortality. Timely identification and elimination of polyps through colonoscopy screening is crucial in order to decrease mortality rates. Accurately detecting polyps in colonoscopy images is difficult because of the differences in characteristics such as size, shape, texture, and similarity to surrounding tissues. Current deep-learning methods often face difficulties in capturing long-range connections necessary for segmentation. This research presents BetterNet, a convolutional neural network (CNN) architecture that combines residual learning and attention methods to enhance the accuracy of polyp segmentation. The primary characteristics encompass (1) a residual decoder architecture that facilitates efficient gradient propagation and integration of multiscale features. (2) channel and spatial attention blocks within the decoder block to concentrate the learning process on the relevant areas of polyp regions. (3) Achieving state-of-the-art performance on polyp segmentation benchmarks while still ensuring computational efficiency. (4) Thorough ablation tests have been conducted to confirm the influence of architectural components. (5) The model code has been made available as open-source for further contribution. Extensive evaluations conducted on datasets such as Kvasir-SEG, CVC ClinicDB, Endoscene, EndoTect, and Kvasir-Sessile demonstrate that BetterNets outperforms current SOTA models in terms of segmentation accuracy by significant margins. The lightweight design enables real-time inference for various applications. BetterNet shows promise in integrating computer-assisted diagnosis techniques to enhance the detection of polyps and the early recognition of cancer. Link to the code: this https URL
Abstract (translated)
直肠癌对癌症相关死亡率的贡献非常大。通过结肠镜筛查及时发现和消除结肠内的结节是降低死亡率的關鍵。然而，准确地在结肠镜图像中检测结节存在很大困难，因为结肠内结节的特征（如大小、形状、质地和与周围组织的相似性）存在差異。目前的大深度学习方法往往在捕捉分割过程中需要的长距离连接方面遇到困难。这项研究提出了BetterNet，一种结合残差学习和关注方法的卷积神经网络（CNN）架构，以提高结肠癌分割的准确性。主要特点包括：（1）一个残差解码器架构，可促进高效的梯度传播和多尺度特征整合。（2）解码器block内的通道和空间关注块，以将学习过程集中在结肠癌区域的 relevant 区域上。（3）在保证准确性的同时提高结肠癌分割基准测试的性能。（4）已经对建筑组件进行了全面消融测试，以确认其影响。（5）模型代码已公开为开源贡献，以进一步发挥其作用。在Kvasir-SEG、CVC诊所数据库、Endoscene、EndoTect和Kvasir-Sessile等数据集上进行的大量评估证明，BetterNets在分割准确性方面显著优于当前的最优模型。轻量级的设计使得各种应用实现实时推理。BetterNet在将计算机辅助诊断技术集成到结肠癌检测和早期癌症识别方面具有前景。链接到代码：https:// this URL
URL
https://arxiv.org/abs/2405.04288
