Figure 3 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer
Year: 2026
Type: other
Abstract: Model interpretation using tile-level embeddings and whole-slide spatial heatmaps. A, UMAP visualization of tile-level histopathology embeddings extracted from H&E-stained WSIs. Points are colored by unsupervised embedding cluster and shaped according to model-defined risk group. B, Distribution of model-predicted risk scores across embedding clusters. Boxplots indicate the median and interquartile range, with pairwise comparison P values shown. C, Representative image tiles from each embedding cluster, illustrating morphologic patterns captured by the image feature extractor.
Authors: Eleftherios P. Mamounas, Ming Chen, Joseph A. Sparano, Md Ashequr Rahman, Yating Cheng, Victoria Wang, Robert J. Gray, Priya Rastogi, Eghbal Amidi, Charles E. Geyer, Tommy Boucher, Tanner J. Freeman, Mohammadreza Ramzanpour, Mukund Varma, Hassan Ghani, Caleb Cheng, Casey Bales, Jennifer R. Ribeiro, Hanna Bandos, Nicolas Stransky, Mark R. Miglarese, Matthew Oberley, David Spetzler, Milan Radovich, George W. Sledge, Norman Wolmark
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7171966797
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