A tool for federated training of segmentation models on whole slide images

Lutnick, Brendon; Manthey, David; Becker, Jan Ulrich; Zuckerman, Jonathan E.; Rodrigues, Luís; Jen, Kuang-Yu; Sarder, Pinaki

doi:10.1016/j.jpi.2022.100101

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…However, neither approach addresses the heterogeneity of participants in federated learning and goes significantly beyond standard weight aggregation algorithms. Recently, [21] have shown in a minimal setting with only three clients that FL can create robustness of deep learning in CP to multi-institutional heterogeneity. Fig.…”

Section: Weight Aggregationmentioning

confidence: 99%

Federated Stain Normalization for Computational Pathology

Wagner

Fuchs

Tolkach

et al. 2022

Lecture Notes in Computer Science

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Although deep federated learning has received much attention in recent years, progress has been made mainly in the context of natural images and barely for computational pathology. However, deep federated learning is an opportunity to create datasets that reflect the data diversity of many laboratories. Further, the effort of dataset construction can be divided among many. Unfortunately, existing algorithms cannot be easily applied to computational pathology since previous work presupposes that data distributions of laboratories must be similar. This is an unlikely assumption, mainly since different laboratories have different staining styles. As a solution, we propose BottleGAN, a generative model that can computationally align the staining styles of many laboratories and can be trained in a privacy-preserving manner to foster federated learning in computational pathology. We construct a heterogenic multi-institutional dataset based on the PESO segmentation dataset and improve the IOU by 42% compared to existing federated learning algorithms. An implementation of BottleGAN is available at https://github.com/MECLabTUDA/BottleGAN.

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Section: Weight Aggregationmentioning

confidence: 99%

Federated Stain Normalization for Computational Pathology

Wagner

Fuchs

Tolkach

et al. 2022

Lecture Notes in Computer Science

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A Comparative Study of Performance Between Federated Learning and Centralized Learning Using Pathological Image of Endometrial Cancer

Yeom,

Kim,

Kim

et al. 2024

J Digit Imaging. Inform. med.

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Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions

Cazzaniga,

Rossi,

Eccher

et al. 2023

J Nephrol

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Introduction Artificial intelligence (AI) integration in nephropathology has been growing rapidly in recent years, facing several challenges including the wide range of histological techniques used, the low occurrence of certain diseases, and the need for data sharing. This narrative review retraces the history of AI in nephropathology and provides insights into potential future developments. Methods Electronic searches in PubMed-MEDLINE and Embase were made to extract pertinent articles from the literature. Works about automated image analysis or the application of an AI algorithm on non-neoplastic kidney histological samples were included and analyzed to extract information such as publication year, AI task, and learning type. Prepublication servers and reviews were not included. Results Seventy-six (76) original research articles were selected. Most of the studies were conducted in the United States in the last 7 years. To date, research has been mainly conducted on relatively easy tasks, like single-stain glomerular segmentation. However, there is a trend towards developing more complex tasks such as glomerular multi-stain classification. Conclusion Deep learning has been used to identify patterns in complex histopathology data and looks promising for the comprehensive assessment of renal biopsy, through the use of multiple stains and virtual staining techniques. Hybrid and collaborative learning approaches have also been explored to utilize large amounts of unlabeled data. A diverse team of experts, including nephropathologists, computer scientists, and clinicians, is crucial for the development of AI systems for nephropathology. Collaborative efforts among multidisciplinary experts result in clinically relevant and effective AI tools. Graphical abstract

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A tool for federated training of segmentation models on whole slide images

Cited by 5 publications

References 27 publications

Federated Stain Normalization for Computational Pathology

Federated Stain Normalization for Computational Pathology

A Comparative Study of Performance Between Federated Learning and Centralized Learning Using Pathological Image of Endometrial Cancer

Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions

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