ScribbleDom: using scribble-annotated histology images to identify domains in spatial transcriptomics data.

ABSTRACT:

Motivation

Spatial domain identification is a very important problem in the field of spatial transcriptomics. The state-of-the-art solutions to this problem focus on unsupervised methods, as there is lack of data for a supervised learning formulation. The results obtained from these methods highlight significant opportunities for improvement.

Results

In this article, we propose a potential avenue for enhancement through the development of a semi-supervised convolutional neural network based approach. Named "ScribbleDom", our method leverages human expert's input as a form of semi-supervision, thereby seamlessly combines the cognitive abilities of human experts with the computational power of machines. ScribbleDom incorporates a loss function that integrates two crucial compo

SUBMITTER: Rahman MN 

PROVIDER: S-EPMC10564617 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature