NANet: Nuclei-Aware Network for Grading of Breast Cancer in HE Stained Pathological Images

Yan, Rui; Li, Jintao; Rao, Xiaosong; Lv, Zhilong; Zheng, Chun-Hou; Dou, Jinjin; Wang, Xiaowen; Ren, Fei; Zhang, Fa

doi:10.1109/bibm49941.2020.9313329

Cited by 12 publications

(14 citation statements)

References 20 publications

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“…The overall patch-wise classification performance of the DANet is demonstrated by the confusion matrix and ROC curve, as shown in Figures 5c and 6c. Methods (BC-Grading) Accuracy (%) AUC Wan et al [10] 69.0 -Yan et al [11] 92.2 0.92 ResNet50 [48] 81.3 0.83 Xception [40] 81.8 0.85 Ours (DANet) 91.6 0.91…”

Section: Nuclei Segmentation Resultsmentioning

confidence: 99%

“…Accuracy (%) AUC Awan et al [44] 90.66 -Hou et al [50] 92.12 -Shaban et al [49] 95.70 -ResNet50 [48] For the three-class BC grading, the state-of-the-art method Nuclei-Aware Network (NANet) was proposed by Yan et al [11], which can learn more nuclei-related features. It…”

Section: Methods (Crc-grading)mentioning

confidence: 99%

“…Wan et al [10] 69.0 -Yan et al [11] 92.2 0.92 ResNet50 [48] 81.3 0.83 Xception [40] 81.8 0.85 Ours (DANet) 91.6 0.91…”

Section: Methods (Bc-grading) Accuracy (%) Aucmentioning

confidence: 99%

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Divide-and-Attention Network for HE-Stained Pathological Image Classification

Yan

Yang

et al. 2022

Biology

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Since pathological images have some distinct characteristics that are different from natural images, the direct application of a general convolutional neural network cannot achieve good classification performance, especially for fine-grained classification problems (such as pathological image grading). Inspired by the clinical experience that decomposing a pathological image into different components is beneficial for diagnosis, in this paper, we propose a Divide-and-Attention Network (DANet) for Hematoxylin-and-Eosin (HE)-stained pathological image classification. The DANet utilizes a deep-learning method to decompose a pathological image into nuclei and non-nuclei parts. With such decomposed pathological images, the DANet first performs feature learning independently in each branch, and then focuses on the most important feature representation through the branch selection attention module. In this way, the DANet can learn representative features with respect to different tissue structures and adaptively focus on the most important ones, thereby improving classification performance. In addition, we introduce deep canonical correlation analysis (DCCA) constraints in the feature fusion process of different branches. The DCCA constraints play the role of branch fusion attention, so as to maximize the correlation of different branches and ensure that the fused branches emphasize specific tissue structures. The experimental results of three datasets demonstrate the superiority of the DANet, with an average classification accuracy of 92.5% on breast cancer classification, 95.33% on colorectal cancer grading, and 91.6% on breast cancer grading tasks.

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Section: Nuclei Segmentation Resultsmentioning

confidence: 99%

Section: Methods (Crc-grading)mentioning

confidence: 99%

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Divide-and-Attention Network for HE-Stained Pathological Image Classification

Yan

Yang

et al. 2022

Biology

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“…In related works, previous approaches have been proposed for automatic histologic grading, including the use of various segmentation techniques, feature sets, group labeling conventions, and classification models [ 36 , 46 , 60 , 61 , 62 , 63 , 64 ]. Wan et al, examined 106 breast tumors and employed a hybrid active contour method to carry out segmentation tasks, using global and local image information [ 60 ].…”

Section: Discussionmentioning

confidence: 99%

“…With respect to histologic grade labels, applying a fixed threshold (0.8) showed an accuracy of 82% for detecting high-grade tumors [ 36 ]. Similarly, Yan et al, implemented an end-to-end computational pipeline, first using a deep learning framework for nuclear segmentation, then a Nuclei-Aware Network (NaNet) consisting of a VGG16 (Visual Geometry Group 16) backbone for feature representation learning [ 63 ]. Their results showed high classification accuracy (92.2%) from the model to distinguish each Nottingham category [ 63 ].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade

et al. 2021

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Background: Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence. Methods: There were 138 patients included in this retrospective study. Breast core biopsy slides were prepared using standard laboratory techniques, digitized, and pre-processed for analysis. Deep convolutional neural networks (CNNs) were developed to identify the regions of interest containing malignant cells and to segment tumor nuclei. Imaging-based features associated with spatial parameters were extracted from the segmented regions of interest (ROIs). Clinical datasets and pathologic biomarkers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) were collected from all study subjects. Pathologic, clinical, and imaging-based features were input into machine learning (ML) models to classify histologic grade, and model performances were tested against ground-truth labels at the patient-level. Classification performances were evaluated using receiver-operating characteristic (ROC) analysis. Results: Multiparametric feature sets, containing both clinical and imaging-based features, demonstrated high classification performance. Using imaging-derived markers alone, the classification performance demonstrated an area under the curve (AUC) of 0.745, while modeling these features with other pathologic biomarkers yielded an AUC of 0.836. Conclusion: These results demonstrate an association between tumor nuclear spatial features and tumor grade. If further validated, these systems may be implemented into pathology CADs and can assist pathologists to expeditiously grade tumors at the time of diagnosis and to help guide clinical decisions.

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