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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN
摘要： Polyps have long been considered as one of the major etiologies to colorectal cancer which is a fatal disease around the world, thus early detection and recognition of polyps plays a crucial role in clinical routines. Accurate diagnoses of polyps through endoscopes operated by physicians becomes a challenging task not only due to the varying expertise of physicians, but also the inherent nature of endoscopic inspections. To facilitate this process, computer-aid techniques that emphasize on fully-conventional image processing and novel machine learning enhanced approaches have been dedicatedly designed for polyp detection in endoscopic videos or images. Among all proposed algorithms, deep learning based methods take the lead in terms of multiple metrics in evolutions for algorithmic performance. In this work, a highly effective model, namely the faster region-based convolutional neural network (Faster R-CNN) is implemented for polyp detection. In comparison with the reported results of the state-of-the-art approaches on polyps detection, extensive experiments demonstrate that the Faster R-CNN achieves very competing results, and it is an efficient approach for clinical practice.
关键词： computer-aided diagnosis,deep learning,Faster R-CNN,polyp detection,endoscopic videos
更新于2025-09-23 15:23:52
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[ACM Press the 3rd International Conference - Seoul, Republic of Korea (2018.08.22-2018.08.24)] Proceedings of the 3rd International Conference on Biomedical Signal and Image Processing - ICBIP '18 - Automatic Detection of Cell Regions in Microscope Images Based on BFED Algorithm
摘要： Circulating tumor cells (CTC) attract attention as a biomarker that can evaluate cancer metastasis and therapeutic effects. The CTC exists in the blood of cancer patients, so pathologists analyze blood by using a fluorescence microscope. However, manual analysis by pathologists is hard-work since the number of CTC to substances contained in the blood is very few and the cell regions are often unclear depending on shooting environments. In addition, there are few studies on automatic identification of CTC. In this paper, we develop an automatic detection method of cell regions in microscope images based on bacterial foraging-based edge detection (BFED) algorithm to analyze CTC. In the first step, we detect the initial cell regions by BFED algorithm. Second, we identify whether the region is a single cell or multiple cells come in connect with other cell(s) by SVM. Third, when a cell is connected with other one, we separate the connecting cells by branch and bound algorithm and obtain the final cell regions. We applied our proposed method to 1680 microscopy images (6 cases). The experimental results demonstrate that the proposed method has a true positive rate of 93.9% and a false positive 1.29 /case.
关键词： Saliency map,Computer aided diagnosis,Support vector machine,Branch and bound algorithm,Circulating tumor cells,BFED algorithm
更新于2025-09-23 15:22:29
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Quantification of the Changes in the Openness of the Major Temporal Arcade in Retinal Fundus Images of Preterm Infants With Plus Disease
摘要： PURPOSE. We tested the hypothesis that the openness of the major temporal arcade (MTA) changes in the presence of plus disease, by quanti?cation via parabolic modeling of the MTA, as well as measurement of an arcade angle for comparative analysis. Such analysis could assist in the detection and treatment of progressive retinopathy of prematurity. METHODS. Digital image processing techniques were applied for the detection and modeling of the MTA via a graphical user interface (GUI) to quantify the openness of the MTA. An arcade angle measure, based on a previously proposed method, also was obtained via the GUI for comparative analysis. The statistical signi?cance of the differences between the plus and no-plus cases for each parameter was analyzed using the P value. The area (Az) under the receiver operating characteristic curve was used to assess the diagnostic performance of each feature. RESULTS. The temporal arcade angle measure and the openness parameter of the parabolic model were used to perform discrimination of plus versus no-plus cases. Using a set of 19 cases with plus and 91 with no plus disease, Az ? 0.70 was obtained using the results of dual-parabolic modeling in screening for plus disease. The arcade angle measure provided comparable results with Az ? 0.73. CONCLUSIONS. Using our proposed image analysis techniques and software, this study demonstrates, for the ?rst time to our knowledge, that the openness of the MTA decreases in the presence of plus disease.
关键词： plus disease,computer-aided diagnosis,retinopathy of prematurity,major temporal arcade,digital image analysis
更新于2025-09-23 15:22:29
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Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network
摘要： Background: In this study, images of 2450 benign thyroid nodules and 2557 malignant thyroid nodules were collected and labeled, and an automatic image recognition and diagnosis system was established by deep learning using the YOLOv2 neural network. The performance of the system in the diagnosis of thyroid nodules was evaluated, and the application value of artificial intelligence in clinical practice was investigated. Methods: The ultrasound images of 276 patients were retrospectively selected. The diagnoses of the radiologists were determined according to the Thyroid Imaging Reporting and Data System; the images were automatically recognized and diagnosed by the established artificial intelligence system. Pathological diagnosis was the gold standard for the final diagnosis. The performances of the established system and the radiologists in diagnosing the benign and malignant thyroid nodules were compared. Results: The artificial intelligence diagnosis system correctly identified the lesion area, with an area under the receiver operating characteristic (ROC) curve of 0.902, which is higher than that of the radiologists (0.859). This finding indicates a higher diagnostic accuracy (p = 0.0434). The sensitivity, positive predictive value, negative predictive value, and accuracy of the artificial intelligence diagnosis system for the diagnosis of malignant thyroid nodules were 90.5%, 95.22%, 80.99%, and 90.31%, respectively, and the performance did not significantly differ from that of the radiologists (p > 0.05). The artificial intelligence diagnosis system had a higher specificity (89.91% vs 77.98%, p = 0.026). Conclusions: Compared with the performance of experienced radiologists, the artificial intelligence system has comparable sensitivity and accuracy for the diagnosis of malignant thyroid nodules and better diagnostic ability for benign thyroid nodules. As an auxiliary tool, this artificial intelligence diagnosis system can provide radiologists with sufficient assistance in the diagnosis of benign and malignant thyroid nodules.
关键词： Thyroid nodules,Ultrasound,Artificial intelligence,Computer-aided diagnosis systems,YOLOv2 neural network
更新于2025-09-23 15:22:29
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Hybrid technique for the detection of suspicious lesions in digital mammograms
摘要： This paper presents an efficient system for the detection of suspicious lesions in mammograms. The proposed detection system consists of three steps. In the first step, an efficient pre-processing technique is developed using Top-Hat morphological filter and NL means filter. In the second step, threshold selection procedure is developed using a combination of Fuzzy C-means (FCM), gradient magnitude (GM), and intensity contrast (IC). Finally, computed threshold is used to extract the suspicious lesions in mammograms. The Free Response Operating Characteristics (FROC) curve is used to assess the performance of the proposed system. Proposed system achieved the sensitivity of 93.8% at the rate of 0.51 false positives per image.
关键词： breast cancer,segmentation,computer-aided diagnosis,fuzzy C-means,mammograms
更新于2025-09-23 15:22:29
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Detection of Circulating Tumor Cells in Fluorescence Microscopy Images Based on ANN Classifier
摘要： Circulating tumor cells (CTCs) is a clinical biomarker for cancer metastasis. CTCs are cells circulating in the body of patients by being separated from primary cancer and entering into blood vessel. CTCs spread every positions in the body, and this is one of the cause of cancer metastasis. To analyze them, pathologists get information about metastasis without invasive test. CTCs test is conducted by analyzing the blood sample from patient. The fluorescence microscope generates a large number of images per each sample, and images contain a lot of cells. There are only a few CTCs in images and cells often have blurry boundaries. So CTCs identification is not an easy work for pathologists. In this paper, we develop an automatic CTCs identification method in fluorescence microscopy images. This proposed method has three section. In the first approach, we conduct the cell segmentation in images by using filtering methods. Next, we compute feature values from each CTC candidate region. Finally, we identify CTCs using artificial neural network algorithm. We apply the proposed method to 5895 microscopy images (7 samplesas), and evaluate the effectiveness of our proposed method by using leave-one-out cross validation. We achieve the result of performance tests, a true positive rate is 92.57% and false positive rate is 9.156%.
关键词： Fluorescence microscopy image,Artificial neural network,Feature analysis,Computer aided diagnosis,Circulating tumor cells
更新于2025-09-23 15:22:29
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Visually interpretable deep network for diagnosis of breast masses on mammograms
摘要： Recently, deep learning technology has achieved various successes in medical image analysis studies including computer-aided diagnosis (CADx). However, current CADx approaches based on deep learning have a limitation in interpreting diagnostic decisions. The limited interpretability is a major challenge for practical use of current deep learning approaches. In this paper, a novel visually interpretable deep network framework is proposed to provide diagnostic decisions with visual interpretation. The proposed method is motivated by the fact that the radiologists characterize breast masses according to the Breast Imaging Reporting and Data System (BIRADS). The proposed deep network framework consists of a BIRADS guided diagnosis network and a BIRADS critic network. A two-dimensional map, named BIRADS guide map, is generated in the inference process of the deep network. The visual features extracted from the breast masses could be refined by the BIRADS guide map, which helps the deep network to focus on more informative areas. The BIRADS critic network makes the BIRADS guide map to be relevant to the characterization of masses in terms of BIRADS description. To verify the proposed method, comparative experiments have been conducted on public mammogram database. On the independent test set (170 malignant masses and 170 benign masses), the proposed method was found to have significantly higher performance compared to the deep network approach without using the BIRADS guide map (p<0.05). Moreover, the visualization was conducted to show the location where the deep network exploited more information. This study demonstrated that the proposed visually interpretable CADx framework could be a promising approach for visually interpreting the diagnostic decision of the deep network.
关键词： breast mass,visual interpretation,computer-aided diagnosis,Deep learning,visualization
更新于2025-09-23 15:21:21
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A computer-aided diagnosis system for HEp-2 fluorescence intensity classification
摘要： Background and objective: The indirect immunofluorescence (IIF) on HEp-2 cells is the recommended technique for the detection of antinuclear antibodies. However, it is burdened by some limitations, as it is time consuming and subjective, and it requires trained personnel. In other fields the adoption of deep neural networks has provided an effective high-level abstraction of the raw data, resulting in the ability to automatically generate optimized high-level features. Methods: To alleviate IIF limitations, this paper presents a computer-aided diagnosis (CAD) system classifying HEp-2 fluorescence intensity: it represents each image using an Invariant Scattering Convolutional Network (Scatnet), which is locally translation invariant and stable to deformations, a characteristic useful in case of HEp-2 samples. To cope with the inter-observer discrepancies found in the dataset, we also introduce a method for gold standard computation that assigns a label and a reliability score to each HEp-2 sample on the basis of annotations provided by expert physicians. Features by Scatnet and gold standard information are then used to train a Support Vector Machine. Results: The proposed CAD is tested on a new dataset of 1771 images annotated by three independent medical centers. The performances achieved by our CAD in recognizing positive, weak positive and negative samples are also compared against those obtained by other two approaches presented so far in the literature. The same system trained on this new dataset is then tested on two public datasets, namely MIVIA and I3Asel. Conclusions: The results confirm the effectiveness of our proposal, also revealing that it achieves the same performance as medical experts.
关键词： HEp-2 samples,Deep learning,Invariant Scattering Convolutional Networks,Computer-aided diagnosis,Indirect immunofluorescence
更新于2025-09-23 15:21:21
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[IEEE 2018 2nd International Conference on Biomedical Engineering (IBIOMED) - Bali, Indonesia (2018.7.24-2018.7.26)] 2018 2nd International Conference on Biomedical Engineering (IBIOMED) - Early Detection of Tuberculosis using Chest X-Ray (CXR) with Computer-Aided Diagnosis
摘要： In this paper, a Computer-aided Diagnosis (CADx) system based on image processing is proposed to assist doctors and radiologists in interpreting Chest X-rays (CXR) for early detection of lung Tuberculosis (TB). CXR can indicate lung abnormalities including TB. However, the interpretations of CXR might vary from one individual to another. It is important to accurately and quickly detect TB because early treatment will prevent more infections and fatal effects from happening. The steps that were performed by the proposed system consisted of preprocessing, segmentation, feature extraction, and classification. In the preprocessing stage homomorphic filter, histogram equalization, median filter, and Contrast-Limited Adaptive Histogram Equalization (CLAHE) were applied to increase image quality. Segmentation was done by using Active Contour Model. Feature extraction was performed by analyzing the image’s first order statistical features. The last stage, classification, was based on the mean values. The results indicated that the system can increase specificity while maintaining sensitivity and accuracy of TB diagnosis. In conclusion, there is a high chance that CADx can assist doctors and radiologists for a more accurate and quick interpretation of CXR in early detection of TB.
关键词： Chest X-ray (CXR),Computer-aided Diagnosis (CADx),Early detection of tuberculosis
更新于2025-09-23 15:21:01
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Coa??Integration of Single Mode Waveguides and Embedded Electrical Interconnects for High Bandwidth Communications
摘要： Valid characterization of carotid atherosclerosis (CA) is a crucial public health issue, which would limit the major risks held by CA for both patient safety and state economies. This paper investigated the unexplored potential of kinematic features in assisting the diagnostic decision for CA in the framework of a computer-aided diagnosis (CAD) tool. To this end, 15 CAD schemes were designed and were fed with a wide variety of kinematic features of the atherosclerotic plaque and the arterial wall adjacent to the plaque for 56 patients from two different hospitals. The CAD schemes were benchmarked in terms of their ability to discriminate between symptomatic and asymptomatic patients and the combination of the Fisher discriminant ratio, as a feature-selection strategy, and support vector machines, in the classification module, was revealed as the optimal motion-based CAD tool. The particular CAD tool was evaluated with several cross-validation strategies and yielded higher than 88% classification accuracy; the texture-based CAD performance in the same dataset was 80%. The incorporation of kinematic features of the arterial wall in CAD seems to have a particularly favorable impact on the performance of image-data-driven diagnosis for CA, which remains to be further elucidated in future prospective studies on large datasets.
关键词： motion analysis,Carotid atherosclerosis (CA),kinematic features,ultrasound (US),computer-aided diagnosis (CAD)
更新于2025-09-23 15:19:57