CausalCellSegmenter: Causal Inference inspired Diversified Aggregation Convolution for Pathology Image Segmentation

arXiv:2403.06066v1 Announce Type: cross
Abstract: Deep learning models have shown promising performance for cell nucleus segmentation in the field of pathology image analysis. However, training a robust model from multiple domains remains a great challenge for cell nucleus segmentation. Additionally, the shortcomings of background noise, highly overlapping between cell nucleus, and blurred edges often lead to poor performance. To address these challenges, we propose a novel framework termed CausalCellSegmenter, which combines Causal Inference Module (CIM) with Diversified Aggregation Convolution (DAC) …

abstract aggregation analysis arxiv causal causal inference challenge convolution cs.cv cs.lg deep learning domains eess.iv however image inference multiple noise pathology performance robust segmentation training type