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全局感知与稀疏特征关联图像级弱监督病理图像分割

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弱监督语义分割方法可以节省大量的人工标注成本,在病理全切片图像(WSI)的分析中有着广泛应用.针对弱监督多实例学习(MIL)方法在病理图像分析中存在的像素实例相互独立缺乏依赖关系,分割结果局部不一致和图像级标签监督信息不充分的问题,该文提出一种全局感知与稀疏特征关联图像级弱监督的端到端多实例学习方法(DASMob-MIL).首先,为克服像素实例之间的独立性,使用局部感知网络提取特征以建立局部像素依赖,并级联交叉注意力模块构建全局信息感知分支(GIPB)以建立全局像素依赖关系.其次,引入像素自适应细化模块(PAR),通过多尺度邻域局部稀疏特征之间的相似性构建亲和核,解决了弱监督语义分割结果局部不一致的问题.最后,设计深度关联监督模块(DAS),通过对多阶段特征图生成的分割图进行加权融合,并使用权重因子关联损失函数以优化训练过程,以降低弱监督图像级标签监督信息不充分的影响.DASMob-MIL模型在自建的结直肠癌数据集YN-CRC和公共弱监督组织病理学图像数据集LUAD-HistoSeg-BC上与其他模型相比展示出了先进的分割性能,模型权重仅为14 MB,在YN-CRC数据集上F1 Score达到了89.5%,比先进的多层伪监督(MLPS)模型提高了3%.实验结果表明,DASMob-MIL仅使用图像级标签实现了像素级的分割,有效改善了弱监督组织病理学图像的分割性能.
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

张印辉、张金凯、何自芬、刘珈岑、吴琳、李振辉、陈光晨

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昆明理工大学机电工程学院 昆明 650500

云南省肿瘤医院病理科 昆明 650106

云南省肿瘤医院放射科 昆明 650106

弱监督语义分割 组织病理学图像 多实例学习 全局感知 稀疏特征

2024

电子与信息学报
中国科学院电子学研究所 国家自然科学基金委员会信息科学部

电子与信息学报

CSTPCD北大核心
影响因子:1.302
ISSN:1009-5896
年,卷(期):2024.46(9)