Boosting Medical Image Segmentation Performance with Adaptive Convolution Layer

arXiv:2404.11361v1 Announce Type: cross
Abstract: 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 …

abstract adaptability analysis and analysis applications arxiv boosting clinical cnns convolution cs.cv eess.iv enabling however image kernel layer medical performance role segmentation success type vital