Looking Under the Hood: Deep Neural Network Visualization to Interpret Whole-Slide Image Analysis Outcomes for Colorectal Polyps

Korbar, Bruno; Olofson, Andrea M.; Miraflor, Allen P.; Nicka, Catherine M; Suriawinata, Matthew A.; Torresani, Lorenzo; Suriawinata, Arief A.; Hassanpour, Saeed

doi:10.1109/cvprw.2017.114

Cited by 35 publications

(44 citation statements)

References 15 publications

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“…Our study not only demonstrates the utility of a deep learning model for classification of colorectal polyps but also advances previous literature in terms of model evaluation and study design. The previous foremost study on deep learning for colorectal polyp classification, done by our team, 26,27 demonstrated good performance on an internal dataset but used a simpler approach and did not include pathologist-level performance or local diagnoses. Our study, on the other hand, evaluates a deep neural network on a multi-institutional external dataset and demonstrates a comparable diagnostic performance of deep neural networks compared to local pathologists at the point-of-care.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of a Deep Neural Network for Automated Classification of Colorectal Polyps on Histopathologic Slides

et al. 2020

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Are deep neural networks trained on data from a single institution for classification of colorectal polyps on digitized histopathology slides generalizable across multiple external institutions? Findings: A new deep neural network was developed based on 326 slide images from our institution to classify the four most common polyp types on digitized histopathology slides. In addition to evaluation on an internal test set of 157 slide images, we evaluated the model on an external test set of 238 slide images from 24 institutions across 13 states in the United States.This model achieved mean accuracies of 93.5% and 87.0% on the internal and external test sets, respectively, which were comparable with the performance of local pathologists on these test sets.Meaning: Deep neural networks could provide a generalizable approach for the classification of colorectal polyps on digitized histopathology slides and if confirmed in clinical trials, could potentially improve the efficiency, reproducibility, and accuracy of one of the most common cancer screening procedures.

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Section: Discussionmentioning

confidence: 99%

Evaluation of a Deep Neural Network for Automated Classification of Colorectal Polyps on Histopathologic Slides

et al. 2020

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“…A few groups have recently applied WSL approaches to medical images, including placenta scans [10], whole-slide images of colorectal cancer [11], diabetic retinopathy [12], microscopic cellular images [13], and lung computed tomography scans [14]. Here, we present a novel model for detection of histological features of glioma on CLE images trained on a dataset of CLE images acquired during brain surgery for this invasive tumor.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike previous models that require patch labeling [11] or an extra step for creating the activation maps during testing [15], our model is solely trained based on the whole image-level labels. Furthermore, we did not limit the network to localize features that are already known phenotypes to the physicians [13,14].…”

Section: Introductionmentioning

confidence: 99%

Weakly-Supervised Learning-Based Feature Localization for Confocal Laser Endomicroscopy Glioma Images

Izadyyazdanabadi

Belykh

Cavallo

et al. 2018

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

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Confocal Laser Endomicroscopy (CLE) is novel handheld fluorescence imaging technology that has shown promise for rapid intraoperative diagnosis of brain tumor tissue. Currently CLE is capable of image display only and lacks an automatic system to aid the surgeon in diagnostically analyzing the images. The goal of this project was to develop a computer-aided diagnostic approach for CLE imaging of human glioma with feature localization function. Despite the tremendous progress in object detection and image segmentation methods in recent years, most of such methods require large annotated datasets for training. However, manual annotation of thousands of histopathology images by physicians is costly and time consuming. To overcome this problem, we constructed a Weakly-Supervised Learning (WSL)-based model for feature localization that trains on image-level annotations, and then localizes incidences of a class-of-interest in the test image. We developed a novel convolutional neural network for diagnostic features localization from CLE images by employing a novel multiscale activation map that is laterally inhibited and collaterally integrated. To validate our method, we compared the model output to the manual annotation performed by four neurosurgeons on test images. The model achieved 88% mean accuracy and 86% mean intersection over union on intermediate features and 87% mean accuracy and 88% mean intersection over union on restrictive fine features, while outperforming other state of the art methods tested. This system can improve accuracy and efficiency in characterization of CLE images of glioma tissue during surgery, and may augment intraoperative decision-making regarding the tumor margin and improve brain tumor resection.

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“…For stomach cancer, Sharma et al 18 used a dataset of 11 WSIs to perform carcinoma classification. For colon cancer, deep learning has been used for predicting survival outcomes 19,20 , classification of nuclei 21 and polyps 10,22 .…”

mentioning

confidence: 99%

Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours

Iizuka¹,

Kanavati²,

Kato

et al. 2020

Sci Rep

325

289

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Histopathological classification of gastric and colonic epithelial tumours is one of the routine pathological diagnosis tasks for pathologists. Computational pathology techniques based on Artificial intelligence (AI) would be of high benefit in easing the ever increasing workloads on pathologists, especially in regions that have shortages in access to pathological diagnosis services. In this study, we trained convolutional neural networks (cnns) and recurrent neural networks (Rnns) on biopsy histopathology whole-slide images (WSis) of stomach and colon. the models were trained to classify WSI into adenocarcinoma, adenoma, and non-neoplastic. We evaluated our models on three independent test sets each, achieving area under the curves (AUCs) up to 0.97 and 0.99 for gastric adenocarcinoma and adenoma, respectively, and 0.96 and 0.99 for colonic adenocarcinoma and adenoma respectively. the results demonstrate the generalisation ability of our models and the high promising potential of deployment in a practical histopathological diagnostic workflow system.

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Looking Under the Hood: Deep Neural Network Visualization to Interpret Whole-Slide Image Analysis Outcomes for Colorectal Polyps

Cited by 35 publications

References 15 publications

Evaluation of a Deep Neural Network for Automated Classification of Colorectal Polyps on Histopathologic Slides

Evaluation of a Deep Neural Network for Automated Classification of Colorectal Polyps on Histopathologic Slides

Weakly-Supervised Learning-Based Feature Localization for Confocal Laser Endomicroscopy Glioma Images

Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours

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