EFCNet：每个特征对医学小物体分割都至关重要
EFCNet: Every Feature Counts for Small Medical Object Segmentation
AI总结：
针对医学小物体分割中编码解码信息丢失问题，提出EFCNet模型，通过跨阶段轴向注意力模块和多精度监督模块减少信息丢失，在两个基准数据集上显著优于现有方法。
摘要：
本文探讨了具有重要临床价值的极小医学物体的分割问题。尽管卷积神经网络（CNN），特别是类似UNet的模型，以及最近的Transformer在图像分割方面取得了显著进展，但我们的实证研究发现，它们在分割本文所关注的医学小物体和病变方面表现不佳。这一局限性可能归因于它们在编码和解码过程中的信息丢失。针对这一挑战，我们提出了一种名为EFCNet的新模型，用于医学图像中的小物体分割。我们的模型包含两个模块：跨阶段轴向注意力模块（CSAA）和多精度监督模块（MPS）。这两个模块分别解决了编码和解码过程中的信息丢失问题。具体而言，CSAA整合了编码器所有阶段的特征，以自适应地学习不同解码阶段所需的合适信息，从而减少编码器中的信息丢失。另一方面，MPS为解码器引入了一种新颖的多精度监督机制。该机制在解码器初始阶段优先关注低分辨率特征，减轻了后续卷积和采样过程造成的信息丢失，并增强了模型的全局感知能力。我们在两个基准医学图像数据集上评估了我们的模型。结果表明，EFCNet显著优于先前为医学和普通图像设计的分割方法。
英文摘要：
This paper explores the segmentation of very small medical objects with significant clinical value. While Convolutional Neural Networks (CNNs), particularly UNet-like models, and recent Transformers have shown substantial progress in image segmentation, our empirical findings reveal their poor performance in segmenting the small medical objects and lesions concerned in this paper. This limitation may be attributed to information loss during their encoding and decoding process. In response to this challenge, we propose a novel model named EFCNet for small object segmentation in medical images. Our model incorporates two modules: the Cross-Stage Axial Attention Module (CSAA) and the Multi-Precision Supervision Module (MPS). These modules address information loss during encoding and decoding procedures, respectively. Specifically, CSAA integrates features from all stages of the encoder to adaptively learn suitable information needed in different decoding stages, thereby reducing information loss in the encoder. On the other hand, MPS introduces a novel multi-precision supervision mechanism to the decoder. This mechanism prioritizes attention to low-resolution features in the initial stages of the decoder, mitigating information loss caused by subsequent convolution and sampling processes and enhancing the model's global perception. We evaluate our model on two benchmark medical image datasets. The results demonstrate that EFCNet significantly outperforms previous segmentation methods designed for both medical and normal images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
