2019/12/01 by Iain Carmichael, Carmichael, Iain, Benjamin C. Calhoun +19
Computer Science · #AI in cancer detection #Applications (stat.AP) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Quantitative Methods (q-bio.QM) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1912.00434
openalex publication_date 2019/12/01 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics.