Towards Spatial Transcriptomics-guided Pathological Image Recognition with Batch-Agnostic Encoder
This work addresses a novel problem in computational pathology by integrating ST data for image recognition, though it appears incremental as it builds on existing contrastive learning methods.
The paper tackles the challenge of leveraging spatial transcriptomics (ST) data for pathological image recognition by addressing batch effects that hinder feature extraction, proposing a batch-agnostic contrastive learning framework that achieved effectiveness on a public dataset.
Spatial transcriptomics (ST) is a novel technique that simultaneously captures pathological images and gene expression profiling with spatial coordinates. Since ST is closely related to pathological features such as disease subtypes, it may be valuable to augment image representation with pathological information. However, there are no attempts to leverage ST for image recognition ({\it i.e,} patch-level classification of subtypes of pathological image.). One of the big challenges is significant batch effects in spatial transcriptomics that make it difficult to extract pathological features of images from ST. In this paper, we propose a batch-agnostic contrastive learning framework that can extract consistent signals from gene expression of ST in multiple patients. To extract consistent signals from ST, we utilize the batch-agnostic gene encoder that is trained in a variational inference manner. Experiments demonstrated the effectiveness of our framework on a publicly available dataset. Code is publicly available at https://github.com/naivete5656/TPIRBAE