A Federated Learning Approach to Tumor Detection in Colon Histology Images.
Federated learning (FL), a relatively new area of research in medical image analysis, enables collaborative learning of a federated deep learning model without sharing the data of participating clients. In this paper, we propose FedDropoutAvg, a new federated learning approach for detection of tumor...
| Published in: | Journal of Medical Systems Vol. 47; no. 1; pp. 1 - 16 |
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| Main Authors: | , , , |
| Format: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
9/16/2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=172971123&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 172971123 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 9/16/2023 vid: 47 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 172971123 172971123 172971123 10.1007/s10916-023-01994-5 172971123 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Federated Learning Approach to Tumor Detection in Colon Histology Images. aug: au: Gunesli, Gozde N. Bilal, Mohsin Raza, Shan E Ahmed Rajpoot, Nasir M. affil: https://ror.org/01a77tt86 The Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK sug: subj: Colorectal Neoplasms Diagnosis Colorectal Neoplasms Pathology Microscopy, Virtual Image Processing, Computer Assisted Federated Learning Diagnosis, Computer Assisted Prediction Models Human Algorithms Comparative Studies Neural Networks (Computer) Experimental Studies Descriptive Statistics Histocytological Preparation Techniques ab: Federated learning (FL), a relatively new area of research in medical image analysis, enables collaborative learning of a federated deep learning model without sharing the data of participating clients. In this paper, we propose FedDropoutAvg, a new federated learning approach for detection of tumor in images of colon tissue slides. The proposed method leverages the power of dropout, a commonly employed scheme to avoid overfitting in neural networks, in both client selection and federated averaging processes. We examine FedDropoutAvg against other FL benchmark algorithms for two different image classification tasks using a publicly available multi-site histopathology image dataset. We train and test the proposed model on a large dataset consisting of 1.2 million image tiles from 21 different sites. For testing the generalization of all models, we select held-out test sets from sites that were not used during training. We show that the proposed approach outperforms other FL methods and reduces the performance gap (to less than 3% in terms of AUC on independent test sites) between FL and a central deep learning model that requires all data to be shared for centralized training, demonstrating the potential of the proposed FedDropoutAvg model to be more generalizable than other state-of-the-art federated models. To the best of our knowledge, ours is the first study to effectively utilize the dropout strategy in a federated setting for tumor detection in histology images. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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