Tens of images can suffice to train neural networks for malignant leukocyte detection

Abstract Convolutional neural networks (CNNs) excel as powerful tools for biomedical image classification. It is commonly assumed that training CNNs requires large amounts of annotated data. This is a bottleneck in many medical applications where annotation relies on expert knowledge. Here, we analy...

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Autores principales:	Jens P. E. Schouten, Christian Matek, Luuk F. P. Jacobs, Michèle C. Buck, Dragan Bošnački, Carsten Marr
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/61eba0d963d64a72884cde1b14ed2b2a
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spelling	oai:doaj.org-article:61eba0d963d64a72884cde1b14ed2b2a2021-12-02T15:51:14ZTens of images can suffice to train neural networks for malignant leukocyte detection10.1038/s41598-021-86995-52045-2322https://doaj.org/article/61eba0d963d64a72884cde1b14ed2b2a2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86995-5https://doaj.org/toc/2045-2322Abstract Convolutional neural networks (CNNs) excel as powerful tools for biomedical image classification. It is commonly assumed that training CNNs requires large amounts of annotated data. This is a bottleneck in many medical applications where annotation relies on expert knowledge. Here, we analyze the binary classification performance of a CNN on two independent cytomorphology datasets as a function of training set size. Specifically, we train a sequential model to discriminate non-malignant leukocytes from blast cells, whose appearance in the peripheral blood is a hallmark of leukemia. We systematically vary training set size, finding that tens of training images suffice for a binary classification with an ROC-AUC over 90%. Saliency maps and layer-wise relevance propagation visualizations suggest that the network learns to increasingly focus on nuclear structures of leukocytes as the number of training images is increased. A low dimensional tSNE representation reveals that while the two classes are separated already for a few training images, the distinction between the classes becomes clearer when more training images are used. To evaluate the performance in a multi-class problem, we annotated single-cell images from a acute lymphoblastic leukemia dataset into six different hematopoietic classes. Multi-class prediction suggests that also here few single-cell images suffice if differences between morphological classes are large enough. The incorporation of deep learning algorithms into clinical practice has the potential to reduce variability and cost, democratize usage of expertise, and allow for early detection of disease onset and relapse. Our approach evaluates the performance of a deep learning based cytology classifier with respect to size and complexity of the training data and the classification task.Jens P. E. SchoutenChristian MatekLuuk F. P. JacobsMichèle C. BuckDragan BošnačkiCarsten MarrNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jens P. E. Schouten Christian Matek Luuk F. P. Jacobs Michèle C. Buck Dragan Bošnački Carsten Marr Tens of images can suffice to train neural networks for malignant leukocyte detection
description	Abstract Convolutional neural networks (CNNs) excel as powerful tools for biomedical image classification. It is commonly assumed that training CNNs requires large amounts of annotated data. This is a bottleneck in many medical applications where annotation relies on expert knowledge. Here, we analyze the binary classification performance of a CNN on two independent cytomorphology datasets as a function of training set size. Specifically, we train a sequential model to discriminate non-malignant leukocytes from blast cells, whose appearance in the peripheral blood is a hallmark of leukemia. We systematically vary training set size, finding that tens of training images suffice for a binary classification with an ROC-AUC over 90%. Saliency maps and layer-wise relevance propagation visualizations suggest that the network learns to increasingly focus on nuclear structures of leukocytes as the number of training images is increased. A low dimensional tSNE representation reveals that while the two classes are separated already for a few training images, the distinction between the classes becomes clearer when more training images are used. To evaluate the performance in a multi-class problem, we annotated single-cell images from a acute lymphoblastic leukemia dataset into six different hematopoietic classes. Multi-class prediction suggests that also here few single-cell images suffice if differences between morphological classes are large enough. The incorporation of deep learning algorithms into clinical practice has the potential to reduce variability and cost, democratize usage of expertise, and allow for early detection of disease onset and relapse. Our approach evaluates the performance of a deep learning based cytology classifier with respect to size and complexity of the training data and the classification task.
format	article
author	Jens P. E. Schouten Christian Matek Luuk F. P. Jacobs Michèle C. Buck Dragan Bošnački Carsten Marr
author_facet	Jens P. E. Schouten Christian Matek Luuk F. P. Jacobs Michèle C. Buck Dragan Bošnački Carsten Marr
author_sort	Jens P. E. Schouten
title	Tens of images can suffice to train neural networks for malignant leukocyte detection
title_short	Tens of images can suffice to train neural networks for malignant leukocyte detection
title_full	Tens of images can suffice to train neural networks for malignant leukocyte detection
title_fullStr	Tens of images can suffice to train neural networks for malignant leukocyte detection
title_full_unstemmed	Tens of images can suffice to train neural networks for malignant leukocyte detection
title_sort	tens of images can suffice to train neural networks for malignant leukocyte detection
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/61eba0d963d64a72884cde1b14ed2b2a
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_version_	1718385635030466560

Tens of images can suffice to train neural networks for malignant leukocyte detection

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