Unsupervised Learning for Cell-Level Visual Representation in Histopathology Images With Generative Adversarial Networks

Hu, Bo; Tang, Ye; Chang, Eric; Fan, Yubo; Lai, Maode; Xu, Yan

doi:10.1109/jbhi.2018.2852639

Cited by 101 publications

(70 citation statements)

References 60 publications

(76 reference statements)

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“…GANs have been used for classification problems as well, either using part of the generator and discriminator as a feature extractor or directly using the discriminator as a classifier (by adding an extra class corresponding to the generated images). Hu et al (2017a) used combined WGAN and InfoGAN for unsupervised celllevel feature representation learning in histopathology images whereas combined WGAN and CatGAN for unsupervised and semi-supervised feature representation learning for dermoscopy images. Both works extract features from the discriminator and build a classifier on top.…”

Section: Classificationmentioning

confidence: 99%

Generative adversarial network in medical imaging: A review

Xin

Walia

Babyn

2019

Medical Image Analysis

1,294

787

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Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into training and imposing higher order consistency. This has proven to be useful in many cases, such as domain adaptation, data augmentation, and image-to-image translation. These properties have attracted researchers in the medical imaging community, and we have seen rapid adoption in many traditional and novel applications, such as image reconstruction, segmentation, detection, classification, and cross-modality synthesis. Based on our observations, this trend will continue and we therefore conducted a review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.

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Section: Classificationmentioning

confidence: 99%

Generative adversarial network in medical imaging: A review

Xin

Walia

Babyn

2019

Medical Image Analysis

1,294

787

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“…A combination of Wasserstein-GAN(WGAN) and information maximizing GAN (InfoGAN) is proposed in [170] for unsupervised cell level feature representation that can be further used for cell-level classification, nuclei segmentation, and cell counting. The proposed system yielded an outstanding performance on bone marrow cellular components.…”

Section: ) Potential Classification Methodsmentioning

confidence: 99%

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Rahaman

et al. 2020

IEEE Access

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Cervical cancer is one of the most common and deadliest cancers among women. Despite that, this cancer is entirely treatable if it is detected at a precancerous stage. Pap smear test is the most extensively performed screening method for early detection of cervical cancer. However, this hand-operated screening approach suffers from a high false-positive result because of human errors. To improve the accuracy and manual screening practice, computer-aided diagnosis methods based on deep learning is developed widely to segment and classify the cervical cytology images automatically. In this survey, we provide a comprehensive study of the state of the art approaches based on deep learning for the analysis of cervical cytology images. Firstly, we introduce deep learning and its simplified architectures that have been used in this field. Secondly, we discuss the publicly available cervical cytopathology datasets and evaluation metrics for segmentation and classification tasks. Then, a thorough review of the recent development of deep learning for the segmentation and classification of cervical cytology images is presented. Finally, we investigate the existing methodology along with the most suitable techniques for the analysis of pap smear cells.

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“…Concerning histological image segmentation specifically, most studies on GAN has focused on cell nucleus segmentation (24–28). Each of them provides slight variations such as the ability to generate more specific images (27), to improve even more the consistency between the mask and the synthesized image (24), or to generate images with positive and/or negative nuclei for IHC staining (28). Concerning the architectures used, the discriminator usually consists of modified versions of classification networks such as the Resnet (29) or the Markovian discriminator (30).…”

Section: Generative Adversarial Network (Gan) To Augment Trainingmentioning

confidence: 99%

Strategies to Reduce the Expert Supervision Required for Deep Learning-Based Segmentation of Histopathological Images

2019

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The emergence of computational pathology comes with a demand to extract more and more information from each tissue sample. Such information extraction often requires the segmentation of numerous histological objects (e.g., cell nuclei, glands, etc.) in histological slide images, a task for which deep learning algorithms have demonstrated their effectiveness. However, these algorithms require many training examples to be efficient and robust. For this purpose, pathologists must manually segment hundreds or even thousands of objects in histological images, i.e., a long, tedious and potentially biased task. The present paper aims to review strategies that could help provide the very large number of annotated images needed to automate the segmentation of histological images using deep learning. This review identifies and describes four different approaches: the use of immunohistochemical markers as labels, realistic data augmentation, Generative Adversarial Networks (GAN), and transfer learning. In addition, we describe alternative learning strategies that can use imperfect annotations. Adding real data with high-quality annotations to the training set is a safe way to improve the performance of a well configured deep neural network. However, the present review provides new perspectives through the use of artificially generated data and/or imperfect annotations, in addition to transfer learning opportunities.

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Unsupervised Learning for Cell-Level Visual Representation in Histopathology Images With Generative Adversarial Networks

Cited by 101 publications

References 60 publications

Generative adversarial network in medical imaging: A review

Generative adversarial network in medical imaging: A review

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Strategies to Reduce the Expert Supervision Required for Deep Learning-Based Segmentation of Histopathological Images

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