Federated Learning for the Classification of Tumor Infiltrating Lymphocytes

Baid, Ujjwal; Pati, Sarthak; Kurç, Tahsin; Gupta, Rajarsi; Bremer, Erich; Abousamra, Shahira; Thakur, Siddhesh; Saltz, Joel; Bakas, Spyridon

doi:10.48550/arxiv.2203.16622

Cited by 5 publications

(6 citation statements)

References 30 publications

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“…This study is meant to be used as an example for future FL studies between collaborators with an inherent amount of trust that can result in clinically deployable ML models. Further research is required to assess privacy concerns in a detailed manner 63,64 and to apply FL to different tasks and data types [66][67][68][69] . Building on this study, a continuous FL consortium would enable downstream quantitative analyses with implications for both routine practice and clinical trials, and most importantly, increase access to high-quality precision care worldwide.…”

Section: Discussionmentioning

confidence: 99%

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Federated learning enables big data for rare cancer boundary detection

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 99%

“…67 Case Comprehensive Cancer Center, Cleveland, OH, USA. 68 Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA. 69 National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA.…”

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confidence: 99%

Federated learning enables big data for rare cancer boundary detection

et al. 2022

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“…The remaining five studies explored a variety of radiology images, including mammography [ 24 , 47 ], prostate MRI [ 23 ], cardiac MRI [ 52 ], and pancreatic CT [ 25 ]. Pathology images were the focus of six studies, which included applications of differential privacy to pathological images [ 28 ], use of the open datasets Camelyon16 and Camelyon17 [ 29 ], analysis of gigapixel whole-slide images [ 30 ], brain pathology segmentation [ 31 ], colorectal cancer data analysis [ 32 ], and examination of tumor-infiltrating lymphocytes in whole-slide images [ 33 ]. Three studies focused on skin images, tackling issues such as skin disease detection using the Dermatology Atlas dataset [ 53 , 54 ] and melanoma detection with the dermoscopic skin lesion image dataset [ 56 ].…”

Section: Resultsmentioning

confidence: 99%

Survey of Medical Applications of Federated Learning

Choi,

Cha,

Lee

et al. 2024

Healthc Inform Res

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Objectives: Medical artificial intelligence (AI) has recently attracted considerable attention. However, training medical AI models is challenging due to privacy-protection regulations. Among the proposed solutions, federated learning (FL) stands out. FL involves transmitting only model parameters without sharing the original data, making it particularly suitable for the medical field, where data privacy is paramount. This study reviews the application of FL in the medical domain.Methods: We conducted a literature search using the keywords "federated learning" in combination with "medical," "healthcare," or "clinical" on Google Scholar and PubMed. After reviewing titles and abstracts, 58 papers were selected for analysis. These FL studies were categorized based on the types of data used, the target disease, the use of open datasets, the local model of FL, and the neural network model. We also examined issues related to heterogeneity and security.Results: In the investigated FL studies, the most commonly used data type was image data, and the most studied target diseases were cancer and COVID-19. The majority of studies utilized open datasets. Furthermore, 72% of the FL articles addressed heterogeneity issues, while 50% discussed security concerns.Conclusions: FL in the medical domain appears to be in its early stages, with most research using open data and focusing on specific data types and diseases for performance verification purposes. Nonetheless, medical FL research is anticipated to be increasingly applied and to become a vital component of multi-institutional research.

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“…Finally, we used a dataset of histology digitized tissue sections stained for H&E, spanning across 12 anatomical sites. The problem at hand was to predict patches containing tumorinfiltrating lymphocytes (TIL) 54 . We observed the best-balanced classification accuracy of 0.89 using a VGG16 that was pretrained on ImageNet 55 and customized for the specific problem.…”

Section: Resultsmentioning

confidence: 99%

GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows

Pati,

Thakur,

Hamamcı

et al. 2023

Commun Eng

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Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms, and the variability of implementations hinders their reproducibility, translation, and deployment. Here we present the community-driven Generally Nuanced Deep Learning Framework (GaNDLF), with the goal of lowering these barriers. GaNDLF makes the mechanism of DL development, training, and inference more stable, reproducible, interpretable, and scalable, without requiring an extensive technical background. GaNDLF aims to provide an end-to-end solution for all DL-related tasks in computational precision medicine. We demonstrate the ability of GaNDLF to analyze both radiology and histology images, with built-in support for k-fold cross-validation, data augmentation, multiple modalities and output classes. Our quantitative performance evaluation on numerous use cases, anatomies, and computational tasks supports GaNDLF as a robust application framework for deployment in clinical workflows.

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Federated Learning for the Classification of Tumor Infiltrating Lymphocytes

Cited by 5 publications

References 30 publications

Federated learning enables big data for rare cancer boundary detection

Federated learning enables big data for rare cancer boundary detection

Survey of Medical Applications of Federated Learning

GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows

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