Abstract Number:
2689
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Zhining Sui (1)
Institutions:
(1) N/A, N/A
First Author:
Presenting Author:
Abstract Text:
Digital pathology is a fast-growing field where leveraging machine learning methods uncovers meaningful imaging features, but it faces the hurdles of sparse annotations, particularly for small pathology image segments. We propose a novel approach that employs spatially resolved transcriptomic data for annotations, though it faces challenges like annotation uncertainty from transcriptomic data and inconsistent image resolutions. We established the viability of this approach and developed a novel transfer-learning neural network model, STpath (Spatial Transcriptomics and pathology images), designed to predict cell type proportions or classify tumor microenvironments. Our findings revealed the intrinsic link between pre-trained deep learning features and cell identities in pathology image segments. Tested on two breast cancer datasets, STpath demonstrated robust performance, effectively handling samples with diverse cell type proportions and high-resolution images despite limited training data. As the influx of spatially resolved transcriptomic data continues, we foresee ongoing updates to STpath, shaping it into an invaluable AI tool for pathologists, enhancing diagnostic accuracy.
Keywords:
Digital and computational pathology|Whole slide imaging|Spatially resolved transcriptomics|Artificial Intelligence|Transfer learning|Cell type proportion and tumor microenvironment
Sponsors:
Section on Statistics in Imaging
Tracks:
Imaging
Can this be considered for alternate subtype?
Yes
Are you interested in volunteering to serve as a session chair?
Yes
I have read and understand that JSM participants must abide by the Participant Guidelines.
Yes
I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.
I understand