Signet Ring Cell Detection with a Semi-supervised Learning Framework

Li, Jiahui; Yang, Shuang; Huang, Xiaodi; Da, Qian; Yang, Xiaoqun; Hu, Zhiqiang; Duan, Qi; Wang, Chaofu; Li, Hongsheng

doi:10.1007/978-3-030-20351-1_66

Cited by 85 publications

(70 citation statements)

References 18 publications

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“…Intuitively, the initial model could predict the nuclei locations on the unlabeled regions. The predicted nuclei are then used to supervise the model training along with the originally labeled nuclei, like what the authors did on cell detection in [30]. However, we find that there are too many false positives among the newly detected nuclei because the trained model with the small number of labeled nuclei is not good.…”

Section: B Self-training With Background Propagationmentioning

confidence: 73%

“…It takes advantage of the mutual information of both tasks to improve performance. Aside from these supervised training methods, Li et al [30] proposed a semi-supervised learning framework for signet ring cell detection to cope with incomplete annotation and make use of unlabeled images. What is more challenging in our case is that we only have a small portion of nuclei annotated.…”

Section: Related Workmentioning

confidence: 99%

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

Huang

et al. 2020

IEEE Trans. Med. Imaging

158

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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Section: B Self-training With Background Propagationmentioning

confidence: 73%

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

158

107

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“…We conducted experiments on the breast cancer dataset of ICIAR 2018 [12], colorectal tumor dataset contributed by ourselves, and colonoscopy tissue dataset of DigestPath 2019 [13], the results demonstrate that our proposed methods significantly improve the performance of deep CNNs on tumor classification. To summarize, the main contributions of this paper are as follows:…”

Section: Introductionmentioning

confidence: 94%

Pathological Image Classification Based on Hard Example Guided CNN

Wang

Peng

Duan³

et al. 2020

IEEE Access

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The diagnosis of biopsy tissue with hematoxylin and eosin (H&E) stained images has been widely used by pathologists to detect the lesions and assess the malignancy. Nevertheless, the diagnostic result relies on the visual observation of pathologists which may vary from person to person under different circumstances. With the advantage of automatically and adaptively learning features at multiple levels of abstraction, Convolutional Neural Networks (CNNs) have rapidly become promising alternatives for pathological image analysis. Therefore, in this paper, we propose an effective method for tumor classification called Hard Example Guided CNN. Our contribution is twofold: firstly, to optimize image representation, we design the CNN architecture as dual-branch, used for extracting global features and local features simultaneously. Secondly, we propose a re-weight training algorithm, which improves learning accuracy and accelerates the convergence by increasing the weight of hard examples. Extensive experiments on multiple datasets demonstrate the superiority of our proposed classification method.

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“…A variety of standard image analysis methods are based on H&E image analysis in order to extract cell nuclei [31,32], with some based on detection [33,34] and other based on segmentation [35,36]. For the reasons mentioned above, segmentation-based methods, including segmentation methods that exploit thresholding, clustering, watershed algorithms, active contours and CNNs, have been preferred over detection-based ones.…”

Section: Related Workmentioning

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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Signet Ring Cell Detection with a Semi-supervised Learning Framework

Cited by 85 publications

References 18 publications

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

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

Pathological Image Classification Based on Hard Example Guided CNN

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

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