Cluster Image Patches with Multiple Mutual Information in Unlabelled Whole-Slide Image | IEEE Conference Publication | IEEE Xplore

Cluster Image Patches with Multiple Mutual Information in Unlabelled Whole-Slide Image


Abstract:

The massive annotation workload has always hindered the progress towards an automatic analysis of gigapixel whole-slide images. Histologically, individual patches from a ...Show More

Abstract:

The massive annotation workload has always hindered the progress towards an automatic analysis of gigapixel whole-slide images. Histologically, individual patches from a constrained spatial region may share rich phenotypic information, where the morphological correlations have the potential to be mined for a grouping or clustering task. In this paper, we propose a clustering technique to extract multiple mutual information from histology images without prior domain knowledge. Specifically, our framework automatically localizes morphologically homogeneous patches within an extended solution space. Our novelty is an expanse and the pattern with which invariant information can be learnt, in contrast to the current literature of feature generation or parametric transformation within an individual patch. Additionally, structure-independent, the model may be applicable to any backbone convolutional neural network architectures. The empirical validation on The Cancer Genome Atlas (TCGA) datasets illustrates an observable margin of patch-level classification accuracy in comparison with state-of-the-art unsupervised approaches.
Date of Conference: 09-12 December 2021
Date Added to IEEE Xplore: 14 January 2022
ISBN Information:
Conference Location: Houston, TX, USA

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