Endoscopy: Computer-Aided Diagnostic System Based on Deep Learning Which Supports Endoscopists’ Decision-Making on the Treatment of Colorectal Polyps

2021 ◽  
pp. 337-342
Author(s):  
Yuichi Mori ◽  
Kensaku Mori
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Diagnostic System ◽  
Colorectal Polyps ◽  
Computer Aided

Convolutional Neural Networks in Computer-Aided Diagnosis of Colorectal Polyps and Cancer: A Review

2021 ◽  
Author(s):  
Kamyab Keshtkar
Keyword(s):  
Colorectal Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Support Vector ◽  
Training Process ◽  
Computer Aided ◽  
Conventional Models ◽  
Aided Diagnosis

As a relatively high percentage of adenoma polyps are missed, a computer-aided diagnosis (CAD) tool based on deep learning can aid the endoscopist in diagnosing colorectal polyps or colorectal cancer in order to decrease polyps missing rate and prevent colorectal cancer mortality. Convolutional Neural Network (CNN) is a deep learning method and has achieved better results in detecting and segmenting specific objects in images in the last decade than conventional models such as regression, support vector machines or artificial neural networks. In recent years, based on the studies in medical imaging criteria, CNN models have acquired promising results in detecting masses and lesions in various body organs, including colorectal polyps. In this review, the structure and architecture of CNN models and how colonoscopy images are processed as input and converted to the output are explained in detail. In most primary studies conducted in the colorectal polyp detection and classification field, the CNN model has been regarded as a black box since the calculations performed at different layers in the model training process have not been clarified precisely. Furthermore, I discuss the differences between the CNN and conventional models, inspect how to train the CNN model for diagnosing colorectal polyps or cancer, and evaluate model performance after the training process.

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

2021 ◽  
Author(s):  
Xin Liao ◽  
Qingli Li ◽  
Xin Zheng ◽  
Jin He
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Atypical Hyperplasia ◽  
Diagnosis And Treatment ◽  
Small Scale ◽  
Prognostic Analysis ◽  
Computer Aided ◽  
Decision Making System ◽  
Suspected Lesion ◽  
Computer Aided Decision

Abstract 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.

scholarly journals Deep Learning Models for Colorectal Polyps

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 245
Author(s):  
Ornela Bardhi ◽  
Daniel Sierra-Sosa ◽  
Begonya Garcia-Zapirain ◽  
Luis Bujanda
Keyword(s):  
Neural Network ◽  
Colorectal Cancer ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Algorithms ◽  
Colorectal Polyps ◽  
Colon Polyps ◽  
Learning Models ◽  
Computer Aided ◽  
Incident Cases

Colorectal cancer is one of the main causes of cancer incident cases and cancer deaths worldwide. Undetected colon polyps, be them benign or malignant, lead to late diagnosis of colorectal cancer. Computer aided devices have helped to decrease the polyp miss rate. The application of deep learning algorithms and techniques has escalated during this last decade. Many scientific studies are published to detect, localize, and classify colon polyps. We present here a brief review of the latest published studies. We compare the accuracy of these studies with our results obtained from training and testing three independent datasets using a convolutional neural network and autoencoder model. A train, validate and test split was performed for each dataset, 75%, 15%, and 15%, respectively. An accuracy of 0.937 was achieved for CVC-ColonDB, 0.951 for CVC-ClinicDB, and 0.967 for ETIS-LaribPolypDB. Our results suggest slight improvements compared to the algorithms used to date.

scholarly journals Computer-aided diagnostic system based on deep learning for classifying colposcopy images

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Lu Liu ◽  
Ying Wang ◽  
Xiaoli Liu ◽  
Sai Han ◽  
Lin Jia ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic System ◽  
Computer Aided

scholarly journals Comparison of Deep Learning Algorithms for Semantic Segmentation of Ultrasound Thyroid Nodules

2021 ◽  
Vol 7 (2) ◽  
pp. 879-882
Author(s):  
Elmer Jeto Gomes Ataide ◽  
Shubham Agrawal ◽  
Aishwarya Jauhari ◽  
Axel Boese ◽  
Alfredol Illanes ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Stratification ◽  
Thyroid Nodules ◽  
Performance Metrics ◽  
Diagnostic System ◽  
Diagnostic Tools ◽  
Observer Variability ◽  
Dice Coefficient ◽  
Computer Aided

Abstract Ultrasound (US) imaging is used as a preliminary diagnostic tool for the detection, risk-stratification and classification of thyroid nodules. In order to perform the risk stratification of nodules in US images physicians first need to effectively detect the nodules. This process is affected due to the presence of inter-observer and intra-observer variability and subjectivity. Computer Aided Diagnostic tools prove to be a step in the right direction towards reducing the issue of subjectivity and observer variability. Several segmentation techniques have been proposed, from these Deep Learning techniques have yielded promising results. This work presents a comparison between four state of the art (SOTA) Deep Learning segmentation algorithms (UNet, SUMNet, ResUNet and Attention UNet). Each network was trained on the same dataset and the results are compared using performance metrics such as accuracy, dice coefficient and Intersection over Union (IoU) to determine the most effective in terms of thyroid nodule segmentation in US images. It was found that ResUNet performed the best with an accuracy, dice coefficient and IoU of 89.2%, 0.857, 0.767. The aim is to use the trained algorithm in the development of a Computer Aided Diagnostic system for the detection, riskstratification and classification of thyroid nodules using US images to reduce subjectivity and observer variability

scholarly journals A Deep-Learning Approach to ECG Classification Based on Adversarial Domain Adaptation

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 437
Author(s):  
Lisha Niu ◽  
Chao Chen ◽  
Hui Liu ◽  
Shuwang Zhou ◽  
Minglei Shu
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Domain Adaptation ◽  
Human Life ◽  
Diagnostic System ◽  
Ecg Signals ◽  
Cross Domain ◽  
Training Samples ◽  
Computer Aided ◽  
Fully Connected

Cardiovascular disease has become one of the main diseases threatening human life and health. This disease is very common and troublesome, and the existing medical resources are scarce, so it is necessary to use a computer-aided automatic diagnosis to overcome these limitations. A computer-aided diagnostic system can automatically diagnose through an electrocardiogram (ECG) signal. This paper proposes a novel deep-learning method for ECG classification based on adversarial domain adaptation, which solves the problem of insufficient-labeled training samples, improves the phenomenon of different data distribution caused by individual differences, and enhances the classification accuracy of cross-domain ECG signals with different data distributions. The proposed method includes three modules: multi-scale feature extraction F, domain discrimination D, and classification C. The module F, constitutive of three different parallel convolution blocks, is constructed to increase the breadth of features extracted from this module. The module D is composed of three convolutional blocks and a fully connected layer, which is to solve the problem of low model layers and low-feature abstraction. In the module C, the time features and the deep-learning extraction features are concatenated on the fully connected layer to enhance feature diversity. The effectiveness of the proposed method is verified by experiments, and the classification accuracy of the experimental electrical signals reaches 92.3%.

scholarly journals Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Eun Mi Song ◽  
Beomhee Park ◽  
Chun-Ae Ha ◽  
Sung Wook Hwang ◽  
Sang Hyoung Park ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Narrow Band Imaging ◽  
Diagnostic System ◽  
Learning Model ◽  
Colorectal Polyps ◽  
Learning Technology ◽  
Submucosal Cancer ◽  
Kappa Value ◽  
Deep Learning Model

AbstractWe aimed to develop a computer-aided diagnostic system (CAD) for predicting colorectal polyp histology using deep-learning technology and to validate its performance. Near-focus narrow-band imaging (NBI) pictures of colorectal polyps were retrieved from the database of our institution. Of these, 12480 image patches of 624 polyps were used as a training set to develop the CAD. The CAD performance was validated with two test datasets of 545 polyps. Polyps were classified into three histological groups: serrated polyp (SP), benign adenoma (BA)/mucosal or superficial submucosal cancer (MSMC), and deep submucosal cancer (DSMC). The overall kappa value measuring the agreement between the true polyp histology and the expected histology by the CAD was 0.614–0.642, which was higher than that of trainees (n = 6, endoscopists with experience of 100 NBI colonoscopies in <6 months; 0.368–0.401) and almost comparable with that of the experts (n = 3, endoscopists with experience of 2,500 NBI colonoscopies in ≥5 years) (0.649–0.735). The areas under the receiver operating curves for CAD were 0.93–0.95, 0.86–0.89, and 0.89–0.91 for SP, BA/MSMC, and DSMC, respectively. The overall diagnostic accuracy of the CAD was 81.3–82.4%, which was significantly higher than that of the trainees (63.8–71.8%, P < 0.01) and comparable with that of experts (82.4–87.3%). The kappa value and diagnostic accuracies of the trainees improved with CAD assistance: that is, the kappa value increased from 0.368 to 0.655, and the overall diagnostic accuracy increased from 63.8–71.8% to 82.7–84.2%. CAD using a deep-learning model can accurately assess polyp histology and may facilitate the diagnosis of colorectal polyps by endoscopists.

scholarly journals Attention U-Net ensemble for interpretable polyp and instrument segmentation

2021 ◽  
Vol 1 (1) ◽  
pp. 47-49
Author(s):  
Michael Yeung
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Colorectal Polyps ◽  
Test Time ◽  
Connected Component ◽  
Improve Performance ◽  
Connected Component Labeling ◽  
Gastrointestinal Pathology ◽  
Computer Aided ◽  
Salient Regions

The difficulty associated with screening and treating colorectal polyps alongside other gastrointestinal pathology presents an opportunity to incorporate computer-aided systems. This paper develops a deep learning pipeline that accurately segments colorectal polyps and various instruments used during endoscopic procedures. To improve transparency, we leverage the Attention U-Net architecture, enabling visualisation of the attention coefficients to identify salient regions. Moreover, we improve performance by incorporating transfer learning using a pre-trained encoder, together with test-time augmentation, softmax averaging, softmax thresholding and connected component labeling to further refine predictions.

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