2024-11-17-fan24a.md

title	booktitle	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
CDNet: Causal Inference inspired Diversified Aggregation Convolution for Pathology Image Segmentation	Proceedings of the MICCAI Workshop on Computational Pathology	Deep learning models have shown promising performance for Nuclei segmentation in the field of pathology image analysis. However, training a robust model from multiple domains remains a great challenge for Nuclei segmentation. Additionally, the shortcomings of background noise, highly overlapping between Nuclei, and blurred edges often lead to poor performance. To address these challenges, we propose a novel framework termed CDNet, which combines Causal Inference Module (CIM) with Diversified Aggregation Convolution (DAC) techniques. The DAC module is designed which incorporates diverse downsampling features through a simple, parameter-free attention module (SimAM), aiming to overcome the problems of edge blurring. Furthermore, we introduce CIM to leverage sample weighting by directly removing the spurious correlations between features for every input sample and concentrating more on the correlation between features and labels. Extensive experiments on the MoNuSeg and GLySAC datasets yielded promising results, with mean intersection over union (mIoU) and Dice similarity coefficient (DSC) scores increasing by 3.59% and 2.61%, and 2.71% and 2.04%, respectively, outperforming other state-of-the-art methods.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	fan24a	0	CDNet: Causal Inference inspired Diversified Aggregation Convolution for Pathology Image Segmentation	51	60	51-60	51	false	Fan, Dawei and Gao, Yifan and Yu, Jiaming and Yang, Changcai and Chen, Riqing and Wei, Lifang	given family Dawei Fan given family Yifan Gao given family Jiaming Yu given family Changcai Yang given family Riqing Chen given family Lifang Wei	2024-11-17		Proceedings of the MICCAI Workshop on Computational Pathology	254	inproceedings	date-parts 2024 11 17	https://raw.githubusercontent.com/mlresearch/v254/main/assets/fan24a/fan24a.pdf