Global Perception and Sparse Feature Associate Image-level Weakly Supervised Pathological Image Segmentation
The weakly supervised semantic segmentation methods have been widely applied in the analysis of Whole Slide Images(WSI),saving a considerable amount of manual annotation costs.Addressing the issues of pixel instance independence,local inconsistency in segmentation results,and insufficient supervision from image-level labels in Multiple-Instance Learning(MIL)methods for pathological image analysis,a novel end-to-end MIL approach named DASMob-MIL is proposed in this paper.Firstly,to overcome the independence among pixel instances,features are extracted using a local perception network to establish local pixel dependencies,while a Global Information Perception Branch(GIPB)is constructed by cascading cross-attention modules to establish global pixel dependencies.Secondly,a Pixel-Adaptive Refinement(PAR)module is introduced to address the problem of local inconsistency in weakly supervised semantic segmentation results by constructing affinity kernels based on the similarity between multi-scale neighborhood local sparse features.Finally,a Deep Association Supervision(DAS)module is designed to optimize the training process by performing weighted fusion on the segmentation maps generated from multi-stage feature maps.Then,employing a weighted factor-associated loss function to mitigate the impact of insufficient supervision from weakly supervised image-level labels.Compared with other models,the DASMob-MIL model demonstrates advanced segmentation performance on the self-built colorectal cancer dataset YN-CRC and the public weakly supervised histopathology image dataset LUAD-HistoSeg-BC,with a model weight of only 14MB and an F1 score of 89.5%on the YN-CRC dataset,which was 3%higher than that of the advanced Multi-Layer Pseudo-Supervision(MLPS)model.Experimental results indicate that DASMob-MIL achieves pixel-level segmentation utilizing only image-level labels,effectively improving the segmentation performance of weakly supervised histopathological images.
Weakly supervised semantic segmentationHistopathological imagesMulti-Instance Learning(MIL)Global perceptionSparse features