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

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

Plasma electrolytic oxidation of magnesium and its alloys: Mechanism, properties and applications

Plasma Electrolyte Oxidation (PEO) process has increasingly been employed to improve magnesium surface properties by fabrication of an MgO-based coating. Originating from conventional anodizing procedures, this high-voltage process produces an adhesive ceramic film on the surface. The present article provides a comprehensive review around mechanisms of PEO coatings fabrication and their different properties. Due to complexity of PEO coatings formation, a complete explanation regarding fabrication mechanisms of PEO coatings has not yet been proposed; however, the most important advancements in the field of fabrication mechanisms of PEO coatings were gathered in this work. Mechanisms of PEO coatings fabrication on magnesium were reviewed considering voltage–time plots, optical spectrometry, acoustic emission spectrometry and electronic properties of the ceramic film. Afterwards, the coatings properties, affecting parameters and improvement strategies were discussed. In addition, corrosion resistance of coatings, important factors in corrosion resistance and methods for corrosion resistance improvement were considered. Tribological properties (important factors and improvement methods) of coatings were also studied. Since magnesium and its alloys are broadly used in biological applications, the biological properties of PEO coatings, important factors in their biological performance and existing methods for improvement of coatings were explained. Addition of ceramic based nanoparticles and formation of nanocomposite coatings may considerably influence properties of plasma electrolyte oxidation coatings. Nanocomposite coatings properties and nanoparticles adsorption mechanisms were included in a separate sector. Another method to improve coatings properties is formation of hybrid coatings on PEO coatings which was discussed in the end. Keywords: Plasma electrolytic oxidation, Magnesium, Tribological properties, Biomedical properties, Nanocomposite coating

The RSNA Cervical Spine Fracture CT Dataset.

This dataset is composed of cervical spine CT images with annotations related to fractures; it is available at https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/