Editorial Computational Pathology

Ciompi, Francesco; Veta, Mitko; Laak, Jeroen van der; Rajpoot, Nasir M.

doi:10.1109/jbhi.2021.3052029

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…2 The computational pathology field has some advantages, such as having large-scale datasets, but the required amount of annotated images is not easily obtained. 9,10 Deep learning (DL) models are data hungry and perform better on diverse and large annotated data. 11 Nevertheless, the medical data cannot be easily shared among the healthcare institutions because of the strategical, economical, ethical, and legal issues.…”

Section: Introductionmentioning

confidence: 99%

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Federated learning‐based colorectal cancer classification by convolutional neural networks and general visual representation learning

Nergiz

2023

Int J Imaging Syst Tech

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Colorectal cancer is the fourth fatal disease in the world, and the massive burden on the pathologists related to the classification of precancerous and cancerous colorectal lesions can be decreased by deep learning (DL) methods. However, the data privacy of the patients is a big challenge for being able to train deep learning models using big medical data. Federated Learning is a rising star in this era by providing the ability to train deep learning models on different sites without sacrificing data privacy. In this study, the Big Transfer model, which is a new General Visual Representation Learning method and six other classical DL methods are converted to the federated version. The effect of the federated learning is measured on all these models on four different data settings extracted from the MHIST and Chaoyang datasets. The proposed models are tested for single learning, centralized learning, and federated learning. The best AUC values of federated learning on Chaoyang are obtained by the Big Transfer and VGG models at 90.77% and 90.76%, respectively, whereas the best AUC value on MHIST is obtained by the Big Transfer model at 89.72%. The overall obtained results of models on all data settings show that the contribution of Federated Learning with respect to single learning is 4.71% and 11.68% for the “uniform” and “label‐biased” data settings of Chaoyang, respectively, and 6.89% for the “difficulty level‐biased” data setting of MHIST. Thus, it is experimentally shown that federated learning can be applied to the field of computational pathology for new institutional collaborations.

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Section: Introductionmentioning

confidence: 99%

“…The computational pathology field has some advantages, such as having large‐scale datasets, but the required amount of annotated images is not easily obtained 9,10 . Deep learning (DL) models are data hungry and perform better on diverse and large annotated data 11 .…”

Section: Introductionmentioning

confidence: 99%

Federated learning‐based colorectal cancer classification by convolutional neural networks and general visual representation learning

Nergiz

2023

Int J Imaging Syst Tech

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“…The manual analysis process of histopathological tissue samples has been shifted to the era of the digital pathology with the innovation of the digital scanners enabling the wholeslide images (WSI) which capture the image from glass slides as a whole [4]. More and more, the digitally acquired tissue images and the rise of the machine learning (ML) and deep learning (DL) algorithms give birth to a novel field called computational pathology (CP) [4][5][6]. In the ML field, as one of the most vibrant fields in academia and the industry, a new sub-branch called federated learning (FL) has been initiated by Google researchers in 2016 [7].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Colorectal Cancer Classification on Highly Class Imbalanced Data Setting via Federated Neural Style Transfer Based Data Augmentation

Nergiz¹

2022

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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.

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