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[ACM Press the 2nd International Conference - Sydney, NSW, Australia (2018.10.06-2018.10.08)] Proceedings of the 2nd International Conference on Graphics and Signal Processing - ICGSP'18 - CNN-Based CAD for Breast Cancer Classification in Digital Breast Tomosynthesis
摘要： Digital breast tomosynthesis (DBT) is a promising new technique for breast cancer diagnosis. DBT has the potential to overcome the tissue superimposition problems that occur on traditional mammograms for tumor detection. However, DBT generates numerous three-dimensional images, thereby creating a heavy workload for radiologists. Therefore, constructing an automatic computer-aided diagnosis (CAD) system for DBT image analysis is necessary. This study compared feature-based CAD and convolutional neural network (CNN)-based CAD for breast cancer classification from DBT images. The research methods included image preprocessing, candidate tumor identification, feature generation, classification, image cropping, augmentation, CNN model design, and deep learning. The accuracy rates (standard deviation) of the CNN- and feature-based CAD for breast cancer classification were 74.85% (0.122) and 87.12% (0.035), respectively. The T value was ?6.229, and the P value was 0.00 < 0.05, which indicated that the CNN-based CAD significantly outperformed feature-based CAD. The results can be applied to clinical medicine and assist radiologists in breast cancer identification.
关键词： computer-aided diagnosis,breast cancer classification,deep learning,Digital breast tomosynthesis
更新于2025-09-11 14:15:04
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[IEEE 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) - Ostrava, Czech Republic (2018.9.17-2018.9.20)] 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) - A Novel Computer-Aided Diagnosis Framework Using Deep Learning for Classification of Fatty Liver Disease in Ultrasound Imaging
摘要： Fatty Liver Disease (FLD), if left untreated can progress into fatal chronic diseases (Eg. fibrosis, cirrhosis, liver cancer, etc.) leading to permanent liver failure. Doctors usually use ultrasound scanning as the primary modality for quantifying the amount of fat deposition in the liver tissues, to categorize the FLD into normal and abnormal. However, this quantification or diagnostic accuracy depends on the expertise and skill of the radiologist. With the advent of Health 4.0 and the Computer Aided Diagnosis (CAD) techniques, the accuracy in detection of FLD using the ultrasound by the sonographers and clinicians can be improved. Along with an accurate diagnosis, the CAD techniques will help radiologists to diagnose more patients in less time. Hence, to improve the classification accuracy of FLD using ultrasound images, we propose a novel CAD framework using convolution neural networks and transfer learning (pre-trained VGG-16 model). Performance analysis shows that the proposed framework offers an FLD classification accuracy of 90.6% in classifying normal and fatty liver images.
关键词： Computer Aided Diagnosis,VGG-16,Deep Learning,Fatty Liver Disease,Ultrasound Imaging
更新于2025-09-10 09:29:36
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Retinal vascular tortuosity assessment: inter-intra expert analysis and correlation with computational measurements
摘要： Background: The retinal vascular tortuosity can be a potential indicator of relevant vascular and non-vascular diseases. However, the lack of a precise and standard guide for the tortuosity evaluation hinders its use for diagnostic and treatment purposes. This work aims to advance in the standardization of the retinal vascular tortuosity as a clinical biomarker with diagnostic potential, allowing, thereby, the validation of objective computational measurements on the basis of the entire spectrum of the expert knowledge. Methods: This paper describes a multi-expert validation process of the computational vascular tortuosity measurements of reference. A group of five experts, covering the different clinical profiles of an ophthalmological service, and a four-grade scale from non-tortuous to severe tortuosity as well as non-tortuous / tortuous and asymptomatic / symptomatic binary classifications are considered for the analysis of the the multi-expert validation procedure. The specialists rating process comprises two rounds involving all the experts and a joint round to establish consensual rates. The expert agreement is analyzed throughout the rating procedure and, then, the consensual rates are set as the reference to validate the prognostic performance of four computational tortuosity metrics of reference. Results: The Kappa indexes for the intra-rater agreement analysis were obtained between 0.35 and 0.83 whereas for the inter-rater agreement in the asymptomatic / symptomatic classification were between 0.22 and 0.76. The Area Under the Curve (AUC) for each expert against the consensual rates were placed between 0.61 and 0.83 whereas the prognostic performance of the best objective tortuosity metric was 0.80. Conclusions: There is a high inter and intra-rater variability, especially for the case of the four grade scale. The prognostic performance of the tortuosity measurements is close to the experts’ performance, especially for Grisan measurement. However, there is a gap between the automatic effectiveness and the expert perception given the lack of clinical criteria in the computational measurements.
关键词： Vascular tortuosity,Retinal circulation,Fundus images,Image analysis,Computer-aided diagnosis
更新于2025-09-10 09:29:36
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Development of an Automated Screening System for Retinopathy of Prematurity Using a Deep Neural Network for Wide-angle Retinal Images
摘要： Background: Retinopathy of prematurity (ROP) is one of the main causes of childhood blindness. However, insufficient ophthalmologists are qualified for ROP screening. Objective: To evaluate the performance of a deep neural network (DNN) for automated screening of ROP. Methods: The training and test sets came from 420,365 wide-angle retina images from ROP screening. A transfer learning scheme was designed to train the DNN classifier. First, a pre-processing classifier images. Then, pediatric ophthalmologists labeled each image as either ROP or negative. The labeled training set (8090 positive images and 9711 negative ones) was used to fine-tune three candidate DNN classifiers (AlexNet, VGG-16, and GoogLeNet) with the transfer learning approach. The resultant classifiers were evaluated on a test data set of 1742 samples, and compared with five independent pediatric retinal ophthalmologists. The ROC (receiver operating characteristic) curve, ROC area under the curve (AUC) and P-R (precision-recall) curve on the test data set were analyzed. Accuracy, precision, sensitivity (recall), specificity, F1 score, Youden index, and MCC (Matthews correlation coefficient) were evaluated at different sensitivity cutoffs. The data from the five pediatric ophthalmologists were plotted in the ROC and P-R curves to visualize their performances. Results: VGG-16 achieved the best performance. At the cutoff point that maximized F1 score in the precision-recall curve, the final DNN model achieved 98.8% accuracy, 94.1% sensitivity, 99.3% specificity, and 93.0% precision. This was comparable to the pediatric ophthalmologists (98.8% accuracy, 93.5% sensitivity, 99.5% specificity and 96.7% precision). Conclusion: In the screening of ROP using the evaluation of wide-angel retinal images, DNNs had high accuracy, sensitivity, specificity, and precision, comparable to that of pediatric ophthalmologists.
关键词： image classification,retinopathy of prematurity,transfer learning,deep neural network,wide-angle retinal image,computer-aided diagnosis
更新于2025-09-09 09:28:46
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Early Diagnosis of Diabetic Retinopathy in OCTA Images Based on Local Analysis of Retinal Blood Vessels and Foveal Avascular Zone
摘要： This paper introduces a diagnosis system for detecting early signs of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA) images. We developed a segmentation technique that was able to extract blood vessels from both retinal superficial and deep maps. It is based on a higher order joint Markov-Gibbs random field (MGRF) model, which combines both current and spatial appearance information of retinal blood vessels. To be able to train/test a support vector machine (SVM) classifier, three local features were extracted from the segmented images. These extracted features are the density and appearance of the retinal blood vessels in addition to the distance map of the foveal avascular zone (FAZ). Then, we used SVM with linear kernel to distinguish sub-clinical DR patients from normal cases. By using 105 subjects, the presented computer-aided diagnosis (CAD) system demonstrated an overall accuracy (ACC) of 97.3% and a Dice similarity coefficient (DSC) of 97.9%.
关键词： Markov-Gibbs random field,support vector machine,diabetic retinopathy,optical coherence tomography angiography,computer-aided diagnosis
更新于2025-09-09 09:28:46
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Pattern Recognition and Signal Analysis in Medical Imaging || Computer-Aided Diagnosis for Diagnostically Challenging Breast Lesions in DCE-MRI
摘要： Breast cancer is the most common cancer among women, but has an encouraging cure rate if diagnosed at an early stage. Thus, early detection of breast cancer continues to be the key for effective treatment. Magnetic resonance (MR) imaging is an emerging and promising new modality for detection and further evaluation of clinically, mammographically, and sonographically occult cancers [134,402]. Acquisition of temporal sequences of between three and six MR images depicting the kinetics of contrast agent molecules in the breast tissue allows for detecting and assessing suspicious tissue disorders with high sensitivity, even in the mammographically dense breasts of young women. Yet, the multitemporal nature of the three-dimensional image data poses new challenges to radiologists as the key-information, reflected by subtle temporal changes of the signal intensity, is only perceivable if all images of the temporal sequence are considered simultaneously.
关键词： Computer-aided diagnosis,Dynamic contrast-enhanced MR,Magnetic resonance imaging,Non-mass-enhancing lesions,Breast cancer
更新于2025-09-04 15:30:14
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An ellipsoid convex enhancement filter for detection of asymptomatic intracranial aneurysm candidates in CAD frameworks
摘要： Various kinds of enhancement filters have been developed in computer-aided diagnostic (CAD) frameworks for asymptomatic intracranial aneurysms in magnetic resonance angiography (MRA). However, many bending or branching portions on vessels are also enhanced by the conventional filters as false positives in 3.0 T MRA, which can visualize smaller vessels compared with 1.5 T MRA. To overcome this problem, this study focused on developing an ellipsoid convex enhancement (ECE) filter, which can selectively enhance aneurysms while reducing false positive contrasts on bending or branching portions on vessels, for detection of asymptomatic intracranial aneurysm candidates in CAD frameworks. The ECE filter was mathematically designed to enhance various convex regions in the intensity space such as convex aneurysms, in which the ratio of the shortest and longest diameters for aneurysms corresponds to the ratio of reciprocals of the square roots of the first and third eigenvalues of a Hessian matrix. The proposed ECE filter was evaluated by measuring an average contrast for false positive models and free-response receiver operating characteristic curves between two simple CAD frameworks using the ECE and conventional filters based on a leave-one-out-by-patient test. MRA images for thirty patients (male: 10, female: 20; age: 48–86 yr, mean: 69.2) with 31 unruptured aneurysms (longest diameter: 2.0–5.5 mm, mean: 3.7 mm) were selected for this study. The average contrast for false positive models was reduced by 51.4% using the ECE filter, compared with the conventional filter for the convex regions with ratios of the shortest and longest diameters less than 0.4. The number of false positives per case was decreased from 41.1 to 22.8 on average at a sensitivity of 87% by using the ECE filter. The ECE filter would be useful for boosting the performance of the CAD framework of asymptomatic intracranial aneurysms by providing higher contrast aneurysms and lower contrast false positives such as bending or branching portions on vessels.
关键词： unruptured intracranial aneurysm,ellipsoid convex enhancement (ECE) filter,magnetic resonance angiography (MRA),computer-aided diagnosis (CAD)
更新于2025-09-04 15:30:14