A Novel Attribute-Based Symmetric Multiple Instance Learning for Histopathological Image Analysis

Vu, Trung; Lai, Phung; Raich, Raviv; Pham, Anh T.; Fern, Xiaoli Z.; Rao, UK Arvind

doi:10.1109/tmi.2020.2987796

Cited by 17 publications

(11 citation statements)

References 39 publications

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“…e MIL has been applied in the pathological field for the WSI analysis [28,34]. In this research, we utilize the MIL algorithm to realize the binary classification (NEC and NEC-free) on the WSIs.…”

Section: Multiple-instancementioning

confidence: 99%

NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images

Liao

Qin

Zheng

2021

Security and Communication Networks

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As a rare malignant tumor, cervical neuroendocrine cancer (NEC) is difficult in diagnosis even for experienced pathologists. A computer-assisted diagnosis may be helpful for the improvement of diagnostic accuracy. Nevertheless, the computer-aided pathological diagnosis has to face a great challenge that the hundred-million-pixels or even gig-pixels whole slide images (WSIs) cannot be applied directly in the existing deep convolution network for training and analysis. Therefore, the construction of a neural network to realize the automatic screening of cervical NEC is challenging; meanwhile, as far as we know, little attention has been paid to this field. In order to address this problem, here we present a multiple-instance learning method for automatic recognition of cervical NEC on pathological WSI, which consists of the Sliding Detector module and Lesion Analyzer module. A pathological WSI dataset, which is composed of 84 NEC cases and 216 NEC-free cases from the Pathological Department of West China Second University Hospital, is applied to evaluate the performance of the method. The experimental results show that the recall rate, accuracy rate, and precision rate of our method for automatic recognition are 92.9%, 92.7%, and 83.0%, respectively, demonstrating the effectiveness and the potential in clinical practice. The application of this method in computer-assisted pathological diagnosis is expected to decrease the misdiagnosis as well as the false diagnosis of rare cervical NEC, and, consequently, improve the therapeutic effect of cervical cancers.

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Section: Multiple-instancementioning

confidence: 99%

NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images

Liao

Qin

Zheng

2021

Security and Communication Networks

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“…Previous studies have described various models and algorithms of multiple instance learning (Campanella et al 2018;Shamsolmoali et al 2021), while MIL has been recently applied for pathological WSI analysis. In endometrial H&E slides, normal and lesioned regions are mixed up, and not all image patches from the WSIs in AH cases contain the lesioned regions (Lotter et al 2021;Vu et al 2020;Yao et al 2020a;Zhang et al 2020). In a practical diagnostic scene, pathologists observe multiple areas in the pathological slides, comprehensively analyze lesioned and normal regions, then give diagnostic conclusions.…”

Section: Multiple Instance Learning (Mil)mentioning

confidence: 99%

Computer-Aided Decision-Making System for Endometrial Atypical Hyperplasia based on Multimodal & Multi-Instance Deep Convolution Networks

Liao

Zheng

et al. 2021

Preprint

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The pathological diagnosis is the gold standard for neoplasms and their precursors, which is highly relevant to the treatment planning and the prognostic analysis. Currently, deep learning networks have been used for the pathological computer-assisted diagnosis and treatment decision-makings. However, due to extremely large size of the whole slide images (WSIs) of pathological slides, the prevailing deep learning models are un-applicable directly in the WSIs analysis. Moreover, the precise exclusion of the blank regions and interfere regions, as well as the manual annotation of various lesioned and normal regions in super large WSIs are infeasible and unavailable in clinical practice. To address aforementioned problems, we develop an computer-aided decision-making system based on multimodal and multi-instance deep convolution networks (CNN) to assist in the diagnosis and treatment of endometrial atypical hyperplasia (AH)/ endometrial intraepithelial hyperplasia (EIH). Firstly, we set up the frame-work of computer-aided decision-making system based on the WSIs image patterns of AH/EIH, and then transfer the large-scale WSI analysis to the small-scale analysis of multiple suspected lesion regions which can be accomplished the major computer vision models, and eventually the results of prognostic analysis for multiple small-scale suspected lesion regions are summarized to obtain the prognostic results of WSIs by the decision supporting algorithm based on the cognition intelligence. We validate the method via experimental analysis of 102 endometrial atypical hyperplasia patients at the West China Second University Hospital of Sichuan University. The performance achieved for endometrial AH/EIH prognostic analysis includes accuracy (85.3%), precision (84.6%), recall (86.3%). Meanwhile, the method has superior performance to prognostic judgment of a single pathologist as well as approximates to analysis results determined by three pathologists according to the majority voting method.

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“…[ 12 ] Multi-instance learning is proposed for image-level classification and annotating relevant regions for histology image analysis. [ 13 ] Focusing on cervical cancer, Wang et al . [ 14 ] presented a block segmentation method to extract textural feature information for CIN classification using support vector machines.…”

Section: Introductionmentioning

confidence: 99%

Automated Cervical Digitized Histology Whole-Slide Image Analysis Toolbox

Sornapudi

Addanki

Stanley

et al. 2021

Journal of Pathology Informatics

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Background: Cervical intraepithelial neoplasia (CIN) is regarded as a potential precancerous state of the uterine cervix. Timely and appropriate early treatment of CIN can help reduce cervical cancer mortality. Accurate estimation of CIN grade correlated with human papillomavirus type, which is the primary cause of the disease, helps determine the patient's risk for developing the disease. Colposcopy is used to select women for biopsy. Expert pathologists examine the biopsied cervical epithelial tissue under a microscope. The examination can take a long time and is prone to error and often results in high inter-and intra-observer variability in outcomes. Methodology: We propose a novel image analysis toolbox that can automate CIN diagnosis using whole slide image (digitized biopsies) of cervical tissue samples. The toolbox is built as a four-step deep learning model that detects the epithelium regions, segments the detected epithelial portions, analyzes local vertical segment regions, and finally classifies each epithelium block with localized attention. We propose an epithelium detection network in this study and make use of our earlier research on epithelium segmentation and CIN classification to complete the design of the end-to-end CIN diagnosis toolbox. Results: The results show that automated epithelium detection and segmentation for CIN classification yields comparable results to manually segmented epithelium CIN classification. Conclusion: This highlights the potential as a tool for automated digitized histology slide image analysis to assist expert pathologists.

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A Novel Attribute-Based Symmetric Multiple Instance Learning for Histopathological Image Analysis

Cited by 17 publications

References 39 publications

NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images

NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images

Computer-Aided Decision-Making System for Endometrial Atypical Hyperplasia based on Multimodal & Multi-Instance Deep Convolution Networks

Automated Cervical Digitized Histology Whole-Slide Image Analysis Toolbox

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A Novel Attribute-Based Symmetric Multiple Instance Learning for Histopathological Image Analysis

Cited by 17 publications

References 39 publications

NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images

NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images

Computer-Aided Decision-Making System for Endometrial Atypical Hyperplasia based on Multimodal &amp; Multi-Instance Deep Convolution Networks

Automated Cervical Digitized Histology Whole-Slide Image Analysis Toolbox

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Computer-Aided Decision-Making System for Endometrial Atypical Hyperplasia based on Multimodal & Multi-Instance Deep Convolution Networks