Let\u27s get hairy : women, body hair and stigma in arts education

A woman who is visibly hairy might be viewed as masculine, dirty, unprofessional, or as a radical feminist. There are variations on what it means to be a woman; feminine does not have to be synonymous with “hairless”. Body hair is a stigma because it is a physical characteristic that is undesirable and shamed when exposed. Body hair as stigma can be explored in terms of creativity and pedagogy. How can creativity work to dismantle stigma? Talking about stigma gives one the chance to express themselves in a way that is exploratory, sparking new ways of understanding. Arts education already possesses qualities that are beneficial to stigma—how can educators and students take advantage of all that creativity has to offer? Creating artwork about women and body hair and analyzing existing works can deepen one’s knowledge of body hair; as a societal form of control and as a lens to look more closely at stigma in arts education. Creativity can be the outlet to find new ways to accept and appreciate the hair on women’s bodies. Creativity can be a valuable tool to address topics that are controversial or simply overlooked. Let’s get hairy

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing