Steven Nelson | Searcharxiv

arXiv2023

RACR-MIL: Rank-aware contextual reasoning for weakly supervised grading of squamous cell carcinoma using whole slide images

Squamous cell carcinoma (SCC) is one of the most common cancer subtype, with an increasing incidence and a significant impact on cancer-related mortality. SCC grading using whole slide images is inherently challenging due to the lack of a standardized grading protocol and substantial tissue heterogeneity. We propose RACR-MIL, a weakly-supervised SCC grading approach that achieves robust generalization across multiple anatomies (skin, head & neck, lung). RACR-MIL is an attention-based multiple-instance learning framework that introduces two key innovations for learning grade-specific contextual representations: (1) a hybrid WSI graph that captures both local tissue context and non-local phenotypic dependencies between tumor regions, and (2) rank-ordering constraints on the attention mechanism that encourage consistent prioritization of higher-grade tumor regions and improve region-level grade confidence, aligning with pathologist's diagnostic process. Our model achieves state-of-the-art performance across multiple SCC datasets, achieving 3-9% improvements over existing methods and up to 10% improvement in tumor localization. In a pilot study, pathologists reported that RACR-MIL improved grading efficiency in 60% of cases, underscoring its potential as a clinically viable cancer diagnosis and grading assistant.

cs.CV↗

arXiv2018

Diversity of Artists in Major U.S. Museums

The U.S. art museum sector is grappling with diversity. While previous work has investigated the demographic diversity of museum staffs and visitors, the diversity of artists in their collections has remained unreported. We conduct the first large-scale study of artist diversity in museums. By scraping the public online catalogs of 18 major U.S. museums, deploying a sample of 10,000 artist records comprising over 9,000 unique artists to crowdsourcing, and analyzing 45,000 responses, we infer artist genders, ethnicities, geographic origins, and birth decades. Our results are threefold. First, we provide estimates of gender and ethnic diversity at each museum, and overall, we find that 85% of artists are white and 87% are men. Second, we identify museums that are outliers, having significantly higher or lower representation of certain demographic groups than the rest of the pool. Third, we find that the relationship between museum collection mission and artist diversity is weak, suggesting that a museum wishing to increase diversity might do so without changing its emphases on specific time periods and regions. Our methodology can be used to broadly and efficiently assess diversity in other fields.

stat.AP↗