IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach

Chen, Haoyuan; Li, Chen; Li, Xiaoyan; Rahaman, Md Mamunur; Hu, Weiming; Li, Yixin; Liu, Wanli; Sun, Changhao; Sun, Hongzan; Huang, Xinyu; Grzegorzek, Marcin

doi:10.1016/j.compbiomed.2022.105265

Cited by 90 publications

(28 citation statements)

References 33 publications

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“…The IC module trains a CNN with the attention areas specified, and then uses ensemble Learning technique based on probability distribution to generate image‐level findings from the CNN's patch‐level output. Chen et al (2022) introduced the IL‐MCAM framework for weakly supervised colorectal histopathology image classification, which is based on attention processes and interactive learning. The framework has two phases: automatic learning (AL) and interactivity learning (IL).…”

Section: Current Research Directionsmentioning

confidence: 99%

A survey on artificial intelligence in histopathology image analysis

Abdelsamea

Zidan

Senousy

et al. 2022

WIREs Data Min & Knowl

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The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning-based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field.

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Section: Current Research Directionsmentioning

confidence: 99%

A survey on artificial intelligence in histopathology image analysis

Abdelsamea

Zidan

Senousy

et al. 2022

WIREs Data Min & Knowl

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“…This subsection first compares the proposed CAST with four well-known vision transformers, ViT ( 11 ), TNT ( 15 ), LeViT ( 14 ), and CrossViT ( 40 ); two classical CNNs, ResNet-101 ( 41 ) and DenseNet-121 ( 42 ); and four state-of-the-art medical image classification methods, GuSA-Net ( 43 ), ROPsNet ( 44 ), CPWA-Net ( 45 ), and IL-MCAM ( 46 ). The quantitative results on the proposed THW dataset are shown in Table 3 ; compared with existing advanced classification methods, the proposed CAST achieves 0.016, 0.012, 0.015, and 0.007 improvements in terms of Rec, Top-1 Acc, Macc, and F1, respectively.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

MC-ViT: Multi-path cross-scale vision transformer for thymoma histopathology whole slide image typing

Zhang

Huang²,

Qin³

et al. 2022

Front. Oncol.

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ObjectivesAccurate histological typing plays an important role in diagnosing thymoma or thymic carcinoma (TC) and predicting the corresponding prognosis. In this paper, we develop and validate a deep learning-based thymoma typing method for hematoxylin & eosin (H&E)-stained whole slide images (WSIs), which provides useful histopathology information from patients to assist doctors for better diagnosing thymoma or TC.MethodsWe propose a multi-path cross-scale vision transformer (MC-ViT), which first uses the cross attentive scale-aware transformer (CAST) to classify the pathological information related to thymoma, and then uses such pathological information priors to assist the WSIs transformer (WT) for thymoma typing. To make full use of the multi-scale (10×, 20×, and 40×) information inherent in a WSI, CAST not only employs parallel multi-path to capture different receptive field features from multi-scale WSI inputs, but also introduces the cross-correlation attention module (CAM) to aggregate multi-scale features to achieve cross-scale spatial information complementarity. After that, WT can effectively convert full-scale WSIs into 1D feature matrices with pathological information labels to improve the efficiency and accuracy of thymoma typing.ResultsWe construct a large-scale thymoma histopathology WSI (THW) dataset and annotate corresponding pathological information and thymoma typing labels. The proposed MC-ViT achieves the Top-1 accuracy of 0.939 and 0.951 in pathological information classification and thymoma typing, respectively. Moreover, the quantitative and statistical experiments on the THW dataset also demonstrate that our pipeline performs favorably against the existing classical convolutional neural networks, vision transformers, and deep learning-based medical image classification methods.ConclusionThis paper demonstrates that comprehensively utilizing the pathological information contained in multi-scale WSIs is feasible for thymoma typing and achieves clinically acceptable performance. Specifically, the proposed MC-ViT can well predict pathological information classes as well as thymoma types, which show the application potential to the diagnosis of thymoma and TC and may assist doctors in improving diagnosis efficiency and accuracy.

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“…In fact, in the field of medicine, convolutional neural network has become a hot topic. From tumor image recognition to gene expression classification, convolutional neural network has made great contributions to medical research [ 51 , 52 ]. In a preexperiment in this paper, we discussed the predictive power of using deep learning for EGFR mutation status.…”

Section: Discussionmentioning

confidence: 99%

Texture Analysis of Enhanced MRI and Pathological Slides Predicts EGFR Mutation Status in Breast Cancer

Zhao

2022

BioMed Research International

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Objective. Image texture information was extracted from enhanced magnetic resonance imaging (MRI) and pathological hematoxylin and eosin- (HE-) stained images of female breast cancer patients. We established models individually, and then, we combine the two kinds of data to establish model. Through this method, we verified whether sufficient information could be obtained from enhanced MRI and pathological slides to assist in the determination of epidermal growth factor receptor (EGFR) mutation status in patients. Methods. We obtained enhanced MRI data from patients with breast cancer before treatment and selected diffusion-weighted imaging (DWI), T1 fast-spin echo (T1 FSE), and T2 fast-spin echo (T2 FSE) as the data sources for extracting texture information. Imaging physicians manually outlined the 3D regions of interest (ROIs) and extracted texture features according to the gray level cooccurrence matrix (GLCM) of the images. For the HE staining images of the patients, we adopted a specific normalization algorithm to simulate the images dyed with only hematoxylin or eosin and extracted textures. We extracted texture features to predict the expression of EGFR. After evaluating the predictive power of each model, the models from the two data sources were combined for remodeling. Results. For enhanced MRI data, the modeling of texture information of T1 FSE had a good predictive effect for EGFR mutation status. For pathological images, eosin-stained images can achieve a better prediction effect. We selected these two classifiers as the weak classifiers of the final model and obtained good results (training group: AUC, 0.983; 95% CI, 0.95-1.00; accuracy, 0.962; specificity, 0.936; and sensitivity, 0.979; test group: AUC, 0.983; 95% CI, 0.94-1.00; accuracy, 0.943; specificity, 1.00; and sensitivity, 0.905). Conclusion. The EGFR mutation status of patients with breast cancer can be well predicted based on enhanced MRI data and pathological data. This helps hospitals that do not test the EGFR mutation status of patients with breast cancer. The technology gives clinicians more information about breast cancer, which helps them make accurate diagnoses and select suitable treatments.

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IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach

Cited by 90 publications

References 33 publications

A survey on artificial intelligence in histopathology image analysis

A survey on artificial intelligence in histopathology image analysis

MC-ViT: Multi-path cross-scale vision transformer for thymoma histopathology whole slide image typing

Texture Analysis of Enhanced MRI and Pathological Slides Predicts EGFR Mutation Status in Breast Cancer

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