Breast Cancer
Overview
The field studies how to detect breast cancer, estimate future risk, classify tumors, and predict treatment response or recurrence. Common approaches include deep learning on mammograms, MRI, and pathology slides; longitudinal alignment of screening images; and models that combine imaging with clinical records or molecular data. Methods are tested on settings such as EMBED and CSAW-CC mammograms, TCGA-BRCA pathology data, and multi-cohort MRI studies, with external validation used to assess transfer across hospitals and populations. Strong results remain difficult to translate reliably: performance can shift across datasets and subgroups, and safety, calibration, interpretability, and prospective clinical benefit remain important tests.
Latest papers
47 papers5 papers in the last four weeks, down 29% on the four weeks before. 0.0% of all new papers.