Intae Moon

Tremendously excited to share our new @CellCellPress article, where we develop TriPath, a method for analyzing 3D pathology samples using weakly supervised AI. Article: authors.elsevier.com/a/1j3RiL7PXqQM-. TriPath enables 3D computational pathology via 3D multiple instance learning allowing AI models to capture intricate morphological details from pathology volumes. Code: github.com/mahmoodlab/Tri… Blog post: linkedin.com/pulse/towards-… Tested on two different imaging modalities, and patient cohorts from two institutions. Our superstar @GreatAndrew90 put in a monumental effort of leading the study, in a fantastic collaboration with @jonliu123 at @UW . Interesting aspects: - Utilizing the whole tissue volume and leveraging 3D deep learning enable superior risk prediction performance compared to 2D deep learning baselines based on a few sampled tissue sections that emulate standard clinical practice. This indicates TriPath can harness additional information provided by 3D tissue morphology. - The performance is also superior to clinical baselines from a reader study that involved six expert pathologists. - The morphologically heterogeneous tissue volume could lead to opposing patient-level outcome predictions, dependent on which portion of the tissue volume is used. This concurs with current clinical literature warning that tissue sampling bias can lead to misdiagnosis. Some limitations: - While the 3D pathology cohort size is unprecedented, it is smaller than typical 2D pathology cohorts. Further large-scale studies will be required for validation. Nevertheless, we believe that this study will initiate a positive cycle, encouraging academic institutions and pharmaceutical companies to contribute large banks of human tissue blocks with paired clinical outcomes, thus speeding up advancements in 3D computational pathology. Concluding insights: We believe that 3D pathology is just around the corner - It has the huge potential to not only augment/improve the current clinical practice centered around 2D examination of human tissue, but also help reveal novel biomarkers for prognosis and therapeutic response. @harvardmed @harvard_data @MassGenBrigham @broadinstitute

🏥📈Announcing our @iclr_conf 2024 Workshop: Time Series for Health in-person in Vienna. We’re looking for short papers on innovative methods, new datasets, and practical applications in health time series data.  Submit by Feb 14!

Check out this #EAAMO2023 paper with @tom_hartvigsen, @LindsaySanneman, @swamiviv1, @ZachHarned, @grace_wickerson, @judywawira, @DrLaurenOR, Leo Anthony Celi, @mattlungrenMD, @julie_a_shah, @MarzyehGhassemi on lessons from aviation for Health AI in regulation, safety, & training!