In situ RNA capturing represents an excellent opportunity for bridging transcriptomic data to a spatial domain, making it possible to map the gene expression to the corresponding anatomical structure. As a result, scientists can better understand the transcriptional heterogeneity with spatially resolved, anatomical, and pathophysiological contexts. However, high throughput sequencing technologies paired with histological images suffer from lower resolution mapping between transcriptome and imaging data. Here, we present Spatial Transcriptome Auto-encoder and Deconvolution (ST-AnD), a scalable deep generative model for predicting gene expression at cellular or nuclei level based on H&E imaging and in situ RNA capturing, thus allowing a better understanding of the tissue microenvironment.
Recently, digital pathology (DP) has been largely improved due to the development of computer vision and
machine learning. Automated detection of high-grade prostate carcinoma (HG-PCa) is an impactful medical
use-case showing the paradigm of collaboration between DP and computer science: given a field of view (FOV)
from a whole slide image (WSI), the computer-aided system is able to determine the grade by classifying the
FOV. Various approaches have been reported based on this approach. However, there are two reasons supporting
us to conduct this work: first, there is still room for improvement in terms of detection accuracy of HG-PCa;
second, a clinical practice is more complex than the operation of simple image classification. FOV ranking is
also an essential step. E.g., in clinical practice, a pathologist usually evaluates a case based on a few FOVs from
the given WSI. Then, makes decision based on the most severe FOV. This important ranking scenario is not
yet being well discussed. In this work, we introduce an automated detection and ranking system for PCa based
on Gleason pattern discrimination. Our experiments suggested that the proposed system is able to perform
high-accuracy detection (~95:57% ± 2:1%) and excellent performance of ranking. Hence, the proposed system
has a great potential to support the daily tasks in the medical routine of clinical pathology.
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