Medical image segmentation plays a vital role in various clinical
applications, enabling accurate delineation and analysis of anatomical
structures or pathological regions. Traditional CNNs have achieved remarkable
success in this field. However, they often rely on fixed kernel sizes, which
can limit their performance and adaptability in medical images where features
exhibit diverse scales and configurations due to variability in equipment,
target sizes, and expert interpretations.
In this paper, we propose an adaptive layer placed ahead of leading
deep-learning models such as UCTransNet, which dynamically adjusts the kernel
size based on the local context of the input image.
By adaptively capturing and fusing features at multiple scales, our approach
enhances the network's ability to handle diverse anatomical structures and
subtle image details, even for recently performing architectures that
internally implement intra-scale modules, such as UCTransnet.
Extensive experiments are conducted on
benchmark medical image datasets to evaluate the effectiveness of our
proposal. It consistently outperforms traditional \glspl{CNN} with fixed kernel
sizes with a similar number of parameters, achieving superior segmentation
Accuracy, Dice, and IoU in popular datasets such as SegPC2021 and ISIC2018. The
model and data are published in the open-source repository, ensuring
transparency and reproducibility of our promising results.

通过在传统 CNN 模型之前放置自适应层，根据输入影像的局部环境动态调整卷积核尺寸，本文提出的方法可以在处理具有多种尺度和配置的医学影像时，增强网络对多样解剖结构和图像细节的处理能力，进而在 SegPC2021 和 ISIC2018 等常见数据集上，取得了优于传统 CNN 模型的分割准确性、Dice 系数和 IoU 的结果。

自适应卷积层优化医学图像分割性能

Boosting Medical Image Segmentation Performance with Adaptive  Convolution Layer

The problem of multi-domain learning of deep networks is considered. An
adaptive layer is induced per target domain and a novel procedure, denoted
covariance normalization (CovNorm), proposed to reduce its parameters. CovNorm
is a data driven method of fairly simple implementation, requiring two
principal component analyzes (PCA) and fine-tuning of a mini-adaptation layer.
Nevertheless, it is shown, both theoretically and experimentally, to have
several advantages over previous approaches, such as batch normalization or
geometric matrix approximations. Furthermore, CovNorm can be deployed both when
target datasets are available sequentially or simultaneously. Experiments show
that, in both cases, it has performance comparable to a fully fine-tuned
network, using as few as 0.13% of the corresponding parameters per target
domain.

本论文研究了深度神经网络的多领域学习问题，并提出了一种自适应层和称作协方差规范化（CovNorm）的简单方法来减少其参数。实验证明，在目标数据集顺序或同时可用的情况下，CovNorm 可以与全面调整的网络具有相当的性能。