Collaborative colorectal cancer classification on highly class imbalanced data setting via federated neural style transfer based data augmentation
Yükleniyor...
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
International Information and Engineering Technology Association
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The deep learning algorithms achieved promising results in the computational pathology in
recent decade but the high data demand of the deep learning algorithms get stuck in the
multi-institutional data collaborations. The federated learning is a novel concept, which
proposes to train the models of the different sites collaboratively via an orchestrating server
without leaking private data. However, the imbalanced data distributions are challenging for
federated learning and result in performance decrease and destabilization. In this study, the
federated version of the neural style transfer algorithm, which was offered by Gatys et al. is
proposed as a data augmentation method on the highly class imbalanced configuration of
Chaoyang colorectal cancer imaging dataset. The proposed method works by firstly
selecting characteristic style images and then generating the gram style matrices on the local
sites and then transferring them to the other imbalanced sites by not leaking any private data.
The proposed method contributed the ACC, F1 Score and AUC results of pure FL by
22.07%, 42.51% and 9.65% using only 20 images for content and 5 images for style.
Additionally, the experiments having different content and style numbers achieved the
satisfactory and consisting results.
Açıklama
Anahtar Kelimeler
Federated learning, Neural style transfer, Convolutional neural network, Colorectal cancer, Computational pathology
Kaynak
Traitement du Signal
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
39
Sayı
6
Künye
Nergiz, M. (2022). Collaborative colorectal cancer classification on highly class imbalanced data setting via federated neural style transfer based data augmentation. Traitement du Signal, 39(6), 2077-2086.
