A novel feature alignment-based cell nucleus identification scheme for medical decision-making systems

AI medical imaging diagnosis enables physicians to rapidly acquire and analyze image information using deep learning techniques. Consequently, it holds a significant position in medical decision-making assistance. Specifically, the complexity and large scale of digital pathology images necessitate accurate recognition of cell arrangement, size, and shape, which aids physicians in identifying cancerous regions. However, existing medical image segmentation techniques often fail to deliver sufficiently detailed results in unknown target domains, resulting in the blurring of cell nucleus boundaries. In order to introduce more display-like information to solve the problem of obscure cell nucleus boundaries, this study proposes an accurate recognition method (UNW) for cell nucleus recognition in medical decision-making systems. It solves the problem of the distribution difference between the source domain and the unknown target domain by inserting feature center alignment (FCA) and feature distribution alignment (FDA) modules into the encoder and decoder. UNW performs instance normalization (IN) and instance whitening (IW) on the feature images to better identify the feature detail information, which results in more distinct boundaries between the cell nucleus and other tissues. Experimental results on various backbone networks show that our method can obtain more accurate decision results. Among them, the IOU reaches 0.704, which outperforms existing models, while the FCA and FDA maintain the segmentation ability of the model in the face of test data that is inconsistent with the distribution of the training data.