Attention U-Net ensemble for interpretable polyp and instrument segmentation

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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Quantifying the uncertainty of deep learning-based computer-aided diagnosis for patient safety

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2019-0057 ◽

2019 ◽

Vol 5 (1) ◽

pp. 223-226

Author(s):

Max-Heinrich Laves ◽

Sontje Ihler ◽

Tobias Ortmaier ◽

Lüder A. Kahrs

Keyword(s):

Monte Carlo ◽

Patient Safety ◽

Deep Learning ◽

Epistemic Uncertainty ◽

Computer Aided Diagnosis ◽

Uncertainty Estimation ◽

Test Time ◽

Prediction Uncertainty ◽

Computer Aided ◽

Aided Diagnosis

AbstractIn this work, we discuss epistemic uncertainty estimation obtained by Bayesian inference in diagnostic classifiers and show that the prediction uncertainty highly correlates with goodness of prediction. We train the ResNet-18 image classifier on a dataset of 84,484 optical coherence tomography scans showing four different retinal conditions. Dropout is added before every building block of ResNet, creating an approximation to a Bayesian classifier. Monte Carlo sampling is applied with dropout at test time for uncertainty estimation. In Monte Carlo experiments, multiple forward passes are performed to get a distribution of the class labels. The variance and the entropy of the distribution is used as metrics for uncertainty. Our results show strong correlation with ρ = 0.99 between prediction uncertainty and prediction error. Mean uncertainty of incorrectly diagnosed cases was significantly higher than mean uncertainty of correctly diagnosed cases. Modeling of the prediction uncertainty in computer-aided diagnosis with deep learning yields more reliable results and is therefore expected to increase patient safety. This will help to transfer such systems into clinical routine and to increase the acceptance of machine learning in diagnosis from the standpoint of physicians and patients.

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

10.20944/preprints202110.0135.v1 ◽

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.

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

10.1007/978-981-16-4325-5_45 ◽

2021 ◽

pp. 337-342

Author(s):

Yuichi Mori ◽

Kensaku Mori

Keyword(s):

Decision Making ◽

Deep Learning ◽

Diagnostic System ◽

Colorectal Polyps ◽

Computer Aided

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45 TOWARDS COMPUTER-AIDED DIAGNOSIS (CAD) FOR COLONOSCOPY: REAL-TIME DIFFERENTIATION OF DIMINUTIVE HYPERPLASTIC COLORECTAL POLYPS AND DIMINUTIVE TRADITIONAL SERRATED ADENOMAS USING A TRANSFER LEARNING BASED DEEP NEURAL NETWORK

Gastroenterology ◽

10.1016/s0016-5085(20)30724-1 ◽

2020 ◽

Vol 158 (6) ◽

pp. S-17

Author(s):

Sungsoo Kim ◽

Sohee Kwon ◽

Mia K. Markey ◽

Alan C. Bovik ◽

Kwang Joon Kim ◽

...

Keyword(s):

Neural Network ◽

Real Time ◽

Transfer Learning ◽

Deep Neural Network ◽

Computer Aided Diagnosis ◽

Colorectal Polyps ◽

Computer Aided ◽

Aided Diagnosis ◽

Serrated Adenomas

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Deep Learning Models for Colorectal Polyps

Information ◽

10.3390/info12060245 ◽

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.

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Hybrid Architecture for Brain Abnormality Detection on brain MR Scans via FastAI

10.21203/rs.3.rs-546190/v1 ◽

2021 ◽

Author(s):

Asmita Dixit

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Transfer Learning ◽

Tumor Classification ◽

Tumor Segmentation ◽

Hybrid Architecture ◽

Brain Tumor Segmentation ◽

Brain Images ◽

Brain Abnormality ◽

Computer Aided

Abstract With lot happening in the field of Deep Learning, classification of brain tumor is still a matter of concern. Brain tumor segmentation and classification using MRI scans has achieved lot of interest in the area of medical imaging. The emphasis still lies on developing automatic computer-aided system for early predictions and diagnosis. MRI of brain Tumors not only varies in shape but sometimes gives less contrasted details also. In this paper, we present a FastAI based Transfer Learning tumor classification in which pre-trained model with segmented features classifies tumor based on its learning. The proposed model with the technique of Deep learning applies ResNet152 as base model to extract features from the MRI brain images. With certain changes in the last 3 layers of ResNet152, 97% accuracy in Dataset-253, 96% accuracy in Dataset-205 is achieved. Models such as Resnet50, VGG16, ResNet34 and Basic CNN is also evaluated. The model improved from ResNet152 has provided improved results. The observations suggest that usage of Transfer Learning is effective when the Dataset is limited. The prepared model is effective and can be collaborated in computer-aided brain MR images Tumor classification.

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Deep Learning-Computer Aided Melanoma Detection Using Transfer Learning

10.1201/9781003194415-10 ◽

2021 ◽

pp. 163-179

Author(s):

Mohan Kumar S. ◽

T. Kumanan ◽

T. R. Ganesh Babu ◽

S. Poovizhi

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Melanoma Detection ◽

Computer Aided

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Multiclassification of Endoscopic Colonoscopy Images Based on Deep Transfer Learning

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/2485934 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Yan Wang ◽

Zixuan Feng ◽

Liping Song ◽

Xiangbin Liu ◽

Shuai Liu

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Classification Accuracy ◽

Dietary Habits ◽

Recognition Rate ◽

Learning Task ◽

Colorectal Polyps ◽

Data Sets ◽

Fine Tune

With the continuous improvement of human living standards, dietary habits are constantly changing, which brings various bowel problems. Among them, the morbidity and mortality rates of colorectal cancer have maintained a significant upward trend. In recent years, the application of deep learning in the medical field has become increasingly spread aboard and deep. In a colonoscopy, Artificial Intelligence based on deep learning is mainly used to assist in the detection of colorectal polyps and the classification of colorectal lesions. But when it comes to classification, it can lead to confusion between polyps and other diseases. In order to accurately diagnose various diseases in the intestines and improve the classification accuracy of polyps, this work proposes a multiclassification method for medical colonoscopy images based on deep learning, which mainly classifies the four conditions of polyps, inflammation, tumor, and normal. In view of the relatively small number of data sets, the network firstly trained by transfer learning on ImageNet was used as the pretraining model, and the prior knowledge learned from the source domain learning task was applied to the classification task about intestinal illnesses. Then, we fine-tune the model to make it more suitable for the task of intestinal classification by our data sets. Finally, the model is applied to the multiclassification of medical colonoscopy images. Experimental results show that the method in this work can significantly improve the recognition rate of polyps while ensuring the classification accuracy of other categories, so as to assist the doctor in the diagnosis of surgical resection.

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Automatic Segmentation of Specific Intervertebral Discs through a Two-Stage MultiResUNet Model

Journal of Clinical Medicine ◽

10.3390/jcm10204760 ◽

2021 ◽

Vol 10 (20) ◽

pp. 4760

Author(s):

Yu-Kai Cheng ◽

Chih-Lung Lin ◽

Yi-Chi Huang ◽

Jui-Chi Chen ◽

Tzu-Peng Lan ◽

...

Keyword(s):

Deep Learning ◽

Automatic Segmentation ◽

Intervertebral Discs ◽

Distance Transform ◽

Learning Models ◽

Connected Component ◽

Two Stage ◽

Connected Component Labeling ◽

Segmentation Accuracy ◽

Deep Learning Model

The automatic segmentation of intervertebral discs from medical images is an important task for an intelligent clinical system. In this study, a deep learning model based on the MultiResUNet model for the automatic segmentation of specific intervertebral discs is presented. MultiResUNet can easily segment all intervertebral discs in MRI images; however, when only certain specific intervertebral discs need to be segmented, problems with segmentation errors, misalignment, and noise occur. In order to solve these problems, a two-stage MultiResUNet model is proposed. Connected-component labeling, automatic cropping, and distance transform are used in the proposed method. The experimental results show that the segmentation errors and misalignments of specific intervertebral discs are greatly reduced, and the segmentation accuracy is increased to about 94%. The performance of the proposed method proves its usefulness for the automatic segmentation of specific intervertebral discs over other deep learning models, such as the U-Net, CNN-based, Attention U-Net, and MultiResUNet models.

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