Robust Histopathology Image Analysis: To Label or to Synthesize?

Hou, Le; Agarwal, Ayush; Samaras, Dimitris; Kurç, Tahsin; Gupta, Rajarsi; Saltz, Joel

doi:10.1109/cvpr.2019.00873

Cited by 109 publications

(75 citation statements)

References 39 publications

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“…Such nuclei are difficult to segment by automated algorithms. In another work, Hou et al (2019a) proposed a GAN architecture for the generation of synthetic tissue images and segmentation masks. The GAN architecture consists of multiple CNNs; a set of CNNs generates and refines synthetic images and masks to reference styles, and another CNN is trained online with these images and masks to generate a segmentation model.…”

Section: Discussionmentioning

confidence: 99%

Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches

et al. 2020

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“…To solve the problem of insufficient training data, Mahmood et al [12] synthesized additional training images using CycleGAN [33]. And Hou et al [15] generated images of different tissue types and adopted an importance sampling loss during segmentation according to the quality of synthesized images. We also use the FCN-based framework, but with weak labels (central points).…”

Section: Related Workmentioning

confidence: 99%

Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images

Huang

et al. 2020

IEEE Trans. Med. Imaging

165

107

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Nuclei segmentation is a fundamental task in histopathology image analysis. Typically, such segmentation tasks require significant effort to manually generate accurate pixel-wise annotations for fully supervised training. To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i.e., only a small portion of nuclei locations in each image are labeled. The framework consists of two learning stages. In the first stage, we design a semi-supervised strategy to learn a detection model from partially labeled nuclei locations. Specifically, an extended Gaussian mask is designed to train an initial model with partially labeled data. Then, selftraining with background propagation is proposed to make use of the unlabeled regions to boost nuclei detection and suppress false positives. In the second stage, a segmentation model is trained from the detected nuclei locations in a weakly-supervised fashion. Two types of coarse labels with complementary information are derived from the detected points and are then utilized to train a deep neural network. The fully-connected conditional random field loss is utilized in training to further refine the model without introducing extra computational complexity during inference. The proposed method is extensively evaluated on two nuclei segmentation datasets. The experimental results demonstrate that our method can achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods while requiring significantly less annotation effort.

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“…Moreover, the performances are bounded by the quality of the annotated images. To solve the problems mentioned above, CycleGANs [48] have been used to create some additional synthetic training images of different tissue types [49] and even coupled with a specific loss function to take into account to the quality of synthesised images [29].…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, the target image database is updated daily with images belonging to different laboratories. Although considerable progress has been achieved so far and some solutions based on domain adaptation (DA) or transfer learning have already been proposed [29,30], cell nuclei segmentation remains a challenging task in the setting mentioned above. In particular, only limited cross-dataset evaluations of existing methods have been provided in the respective papers, partly due to the small number and tiny size of publicly available datasets, but mainly because the authors focused their attention on DA problems similar to other fields of applications, neglecting the real DA problems that can be present in this field of application.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario

Putzu

Fumera

2020

Applied Sciences

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Cell nuclei segmentation is a challenging task, especially in real applications, when the target images significantly differ between them. This task is also challenging for methods based on convolutional neural networks (CNNs), which have recently boosted the performance of cell nuclei segmentation systems. However, when training data are scarce or not representative of deployment scenarios, they may suffer from overfitting to a different extent, and may hardly generalise to images that differ from the ones used for training. In this work, we focus on real-world, challenging application scenarios when no annotated images from a given dataset are available, or when few images (even unlabelled) of the same domain are available to perform domain adaptation. To simulate this scenario, we performed extensive cross-dataset experiments on several CNN-based state-of-the-art cell nuclei segmentation methods. Our results show that some of the existing CNN-based approaches are capable of generalising to target images which resemble the ones used for training. In contrast, their effectiveness considerably degrades when target and source significantly differ in colours and scale.

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Robust Histopathology Image Analysis: To Label or to Synthesize?

Cited by 109 publications

References 39 publications

Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches

Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches

Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images

An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario

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