Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing.

An Adaptive Cluster-based Filtering Framework for Speckle Reduction of OCT Skin Images

Optical coherence tomography (OCT) has become a favorable device in the Dermatology discipline due to its moderate resolution and penetration depth. OCT images however contain a grainy pattern, called speckle, due to the use of a broadband source in the configuration of OCT. So far, a variety of filtering (de-speckling) techniques is introduced to reduce speckle in OCT images. Most of these methods are generic and can be applied to OCT images of different tissues. The ambition of this work is to provide a de-speckling framework specialized for filtering skin tissues for the community to utilize, adapt or build upon. In this paper, we present an adaptive cluster-based filtering framework, optimized for speckle reduction of OCT skin images. In this framework, by considering the layered structure of skin, first the OCT skin images are segmented into differentiable layers utilizing clustering algorithms, and then each cluster is de-speckled individually using adaptive filtering techniques. In this study, hierarchical clustering algorithm and adaptive Wiener filtering technique are utilized to develop the framework. The proposed method is tested on optical solid phantoms with predetermined optical properties. The method is also tested on healthy human skin images. The results show that the proposed cluster-based filtering method can effectively reduce the speckle and increase the signal-to-noise ratio and contrast while preserving the edges in the image. The proposed cluster-based filtering framework enables researchers to develop unsupervised learning solutions for de-speckling OCT skin images using adaptive filtering methods, or extend the framework to new applications.