RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification

Wang, Shujun; Zhu, Yaxi; Yu, Lequan; Chen, Hao; Lin, Huangjing; Wan, Xiang-Bo; Fan, Xinjuan; Heng, Pheng‐Ann

doi:10.1016/j.media.2019.101549

Cited by 148 publications

(88 citation statements)

References 25 publications

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“…Wang et al. [25] classified gastric lesions into normal, dysplasia, and cancer with an accuracy of 86.5% and Tomita et al. [26] classified esophagus lesions into Barrett esophagus, dysplasia, and cancer with an accuracy of 83%.…”

Section: Application Of Dl‐based Ai In Tumor Pathologymentioning

confidence: 99%

Emerging role of deep learning‐based artificial intelligence in tumor pathology

Jiang

Yue

Wang

et al. 2020

Cancer Communications

237

147

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The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)-based AI, in tumor pathology. The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology, including tumor diagnosis, subtyping, grading, staging, and prognostic prediction, as well as the identification of pathological features, biomarkers and genetic changes. The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks. In addition, AI is useful for pathologists to meet the requirements of precision oncology. However, there are still some challenges relating to the implementation of AI, including the issues of algorithm validation and interpretability, computing systems, the unbelieving attitude of pathologists, clinicians and patients, as well as regulators and reimbursements. Herein, we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.

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Section: Application Of Dl‐based Ai In Tumor Pathologymentioning

confidence: 99%

Emerging role of deep learning‐based artificial intelligence in tumor pathology

Jiang

Yue

Wang

et al. 2020

Cancer Communications

237

147

View full text Add to dashboard Cite

show abstract

“…Pathology application of artificial intelligence image analysis in gastric cancer classification from WSIs, including discriminative patch instance detection (Stage 1) and whole‐slide image‐level classification (Stage 2) from the RMDL 27 method. Visualization of DL prediction results and ground‐truth annotations shows high concordance.…”

Section: Artificial Intelligence Image Analysis In Pathologymentioning

confidence: 99%

“…A series of robust and scalable AI methods have been developed in this direction during the last few years. Wang et al 27 proposed a recalibrated multi-instance DL network for gastric cancer WSI classification (Fig. 3).…”

Section: Artificial Intelligence Image Analysis In Pathologymentioning

confidence: 99%

Potentials of AI in medical image analysis in Gastroenterology and Hepatology

Chen

Sung

2021

J of Gastro and Hepatol

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With the advancement of artificial intelligence (AI) technology, it comes in a big wave carrying possibly huge impact in the field of medicine. Gastroenterology and hepatology, being a specialty relying much on diagnostic imaging, endoscopy, and histopathology, AI technology has promised improving the quality and consistency of care to the patients. In this review, we will elucidate the development of machine learning methods, especially the visual representation mechanism in deep learning on recognition tasks. Various AI‐image analysis applications in endoscopy, radiology, and pathology are covered in gastroenterology and hepatology and reveal the enormous potentials for AI in assisting diagnosis, prognosis, and treatment. We also discuss the promises as well as pitfalls for AI in medical image analysis and pointing out future research directions.

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“…Li [ 27 ] developed a graph convolutional neural network to learn global topological representations of WSI for providing more accurate survival risk predictions. Wang [ 28 ] proposed a recalibrated multi-instance network for adaptively aggregating the patch information to image-level prediction of whole slide gastric image, which improved image-level classification accuracy by assigning different weights to each instance. Sun [ 29 ] applied U-Net to extract pixel-level features and adopt multiple classic fine-tuned CNN to obtain patch-level features, then jointed them by a hierarchical conditional random field method to localize abnormal (cancer) regions in gastric histopathology images.…”

Section: Introductionmentioning

confidence: 99%

A fast and effective detection framework for whole-slide histopathology image analysis

Ruan

Zhu

et al. 2021

PLoS ONE

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Pathologists generally pan, focus, zoom and scan tissue biopsies either under microscopes or on digital images for diagnosis. With the rapid development of whole-slide digital scanners for histopathology, computer-assisted digital pathology image analysis has attracted increasing clinical attention. Thus, the working style of pathologists is also beginning to change. Computer-assisted image analysis systems have been developed to help pathologists perform basic examinations. This paper presents a novel lightweight detection framework for automatic tumor detection in whole-slide histopathology images. We develop the Double Magnification Combination (DMC) classifier, which is a modified DenseNet-40 to make patch-level predictions with only 0.3 million parameters. To improve the detection performance of multiple instances, we propose an improved adaptive sampling method with superpixel segmentation and introduce a new heuristic factor, local sampling density, as the convergence condition of iterations. In postprocessing, we use a CNN model with 4 convolutional layers to regulate the patch-level predictions based on the predictions of adjacent sampling points and use linear interpolation to generate a tumor probability heatmap. The entire framework was trained and validated using the dataset from the Camelyon16 Grand Challenge and Hubei Cancer Hospital. In our experiments, the average AUC was 0.95 in the test set for pixel-level detection.

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RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification

Cited by 148 publications

References 25 publications

Emerging role of deep learning‐based artificial intelligence in tumor pathology

Emerging role of deep learning‐based artificial intelligence in tumor pathology

Potentials of AI in medical image analysis in Gastroenterology and Hepatology

A fast and effective detection framework for whole-slide histopathology image analysis

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