Evaluation of an Artificial Intelligence–Augmented Digital System for Histologic Classification of Colorectal Polyps

Nasir-Moin, Mustafa; Suriawinata, Arief A.; Ren, Bing; Li, Xiaoying; Robertson, Douglas J.; Bagchi, Srishti; Tomita, Naofumi; Wei, Jason; MacKenzie, Todd A.; Rees, Judy R.; Hassanpour, Saeed

doi:10.1001/jamanetworkopen.2021.35271

Cited by 23 publications

(17 citation statements)

References 38 publications

(91 reference statements)

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“…Deep neural networks have been proved a powerful tool in computer vision and are increasingly applied in medical image analysis [15][16][17] . In the histologic image inference domain, deep convolutional neural networks are applied as a backbone for whole-slide image analysis and classification 18,19 .…”

Section: Deep Convolutional Neural Networkmentioning

confidence: 99%

Graph Convolutional Neural Networks for Histological Classification of Pancreatic Cancer

Hamilton

et al. 2022

Preprint

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Pancreatic ductal adenocarcinoma has some of the worst prognostic outcomes among various cancer types. Detection of histologic patterns of pancreatic tumors is essential to predict prognosis and decide about the treatment for patients. This histologic classification can have a large degree of variability even among expert pathologists. This study proposes a graph convolutional network-based deep learning model to detect aggressive adenocarcinoma and less aggressive pancreatic tumors from benign cases. Our model uses a convolutional neural network to extract detailed information from every small region in a whole-slide image. Then, we use a graph architecture to aggregate the extracted features from these regions and their positional information to capture the whole-slide level structure and make the final prediction. We evaluated our model on an independent test set and achieved an F1 score of 0.85 for detecting neoplastic cells and ductal adenocarcinoma, significantly outperforming other baseline methods. If validated in prospective studies, this approach has a great potential to assist pathologists in identifying adenocarcinoma and other types of pancreatic tumors in clinical settings.

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Section: Deep Convolutional Neural Networkmentioning

confidence: 99%

Graph Convolutional Neural Networks for Histological Classification of Pancreatic Cancer

Hamilton

et al. 2022

Preprint

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“…The computer aided diagnosis systems can ease this labor-intensive work and minimize the mistakes of the traditional approaches. Therefore, employing a computer aided diagnosis system, in which differentiation of non-adenomatous and adenomatous biopsies is automated, would be beneficial [4].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, DL based approaches are actively employed for pattern classification or analysis of the medical images. There are extensive studies conducted with numerous methods focusing on the individual diagnosis of colorectal cancer from histopathology images, such as classification of colorectal adenocarcinoma [ [2], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15]], colon polyp classification [ [16], [17], [4], [18], [19], [20], [21]] and colon gland classification [ [5], [22], [23]].…”

Section: Introductionmentioning

confidence: 99%

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Classification of Colorectal Polyps from Histopathological Images using Ensemble of ConvNeXt Variants

Tasdemir

Akay

Doğan

et al. 2022

Preprint

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In this work, we propose a computer-aided diagnosis system for automatic classification of adenomatous polyps from colonic histological images. The proposed method employs an ensemble of ConvNeXt variants, which is one of the most recent and prominent convolution based deep learning architectures. Comprehensive experiments show that classification performance of the proposed method outperforms the state-of-the-art Deep CNN models on our dataset, with a 95% accuracy. It achieves 91.1% and 90% classification accuracy respectively on EBHI and UniToPatho datasets, which shows that the model can generalize well for other datasets as well. Furthermore, we investigate the effect of stain normalization preprocessing on the classification accuracy. While the other methods are sensitive to the normalization, the proposed method retains its accuracy levels under the variations, which also indicates a high generalization ability. Lastly, we use the Grad-Cam method and show that the proposed model gives more attention to the regions where the cancer indicators potentially reside.

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“…A recent study showed that the use of AI improved the accuracy of 4-class differentiation of pathologic diagnoses from 74% to 81%. 13 The combined use of semisupervised learning and AI-aided pathologic diagnosis may be the best solution to overcome this problem.…”

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confidence: 99%

Artificial intelligence for disease diagnosis: the criterion standard challenge

Mori

Misawa

Bernal

et al. 2022

Gastrointestinal Endoscopy

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Evaluation of an Artificial Intelligence–Augmented Digital System for Histologic Classification of Colorectal Polyps

Abstract: This diagnostic study evaluates the accuracy and assessment time of an artificial intelligence (AI)–augmented digital system compared with standard microscopic assessment for interpretation of slides with colorectal polyp samples.

Cited by 23 publications

References 38 publications

Graph Convolutional Neural Networks for Histological Classification of Pancreatic Cancer

Graph Convolutional Neural Networks for Histological Classification of Pancreatic Cancer

Classification of Colorectal Polyps from Histopathological Images using Ensemble of ConvNeXt Variants

Artificial intelligence for disease diagnosis: the criterion standard challenge

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