Boosting Medical Image Segmentation Performance with Adaptive
  Convolution Layer
Seyed M. R. Modaresi Aomar Osmani Mohammadreza Razzazi Abdelghani Chibani
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.