Journal ArticleDOI
Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks
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This study corroborates that very deep CNNs with effective training mechanisms can be employed to solve complicated medical image analysis tasks, even with limited training data.Abstract:
Automated melanoma recognition in dermoscopy images is a very challenging task due to the low contrast of skin lesions, the huge intraclass variation of melanomas, the high degree of visual similarity between melanoma and non-melanoma lesions, and the existence of many artifacts in the image. In order to meet these challenges, we propose a novel method for melanoma recognition by leveraging very deep convolutional neural networks (CNNs). Compared with existing methods employing either low-level hand-crafted features or CNNs with shallower architectures, our substantially deeper networks (more than 50 layers) can acquire richer and more discriminative features for more accurate recognition. To take full advantage of very deep networks, we propose a set of schemes to ensure effective training and learning under limited training data. First, we apply the residual learning to cope with the degradation and overfitting problems when a network goes deeper. This technique can ensure that our networks benefit from the performance gains achieved by increasing network depth. Then, we construct a fully convolutional residual network (FCRN) for accurate skin lesion segmentation, and further enhance its capability by incorporating a multi-scale contextual information integration scheme. Finally, we seamlessly integrate the proposed FCRN (for segmentation) and other very deep residual networks (for classification) to form a two-stage framework. This framework enables the classification network to extract more representative and specific features based on segmented results instead of the whole dermoscopy images, further alleviating the insufficiency of training data. The proposed framework is extensively evaluated on ISBI 2016 Skin Lesion Analysis Towards Melanoma Detection Challenge dataset. Experimental results demonstrate the significant performance gains of the proposed framework, ranking the first in classification and the second in segmentation among 25 teams and 28 teams, respectively. This study corroborates that very deep CNNs with effective training mechanisms can be employed to solve complicated medical image analysis tasks, even with limited training data.read more
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Dermoscopy diagnosis of cancerous lesions utilizing dual deep learning algorithms via visual and audio (sonification) outputs: Laboratory and prospective observational studies.
TL;DR: Adding a second stage of processing, which includes a deep learning algorithm of sonification and heuristic inspection with machine learning, significantly improves diagnostic accuracy and is expected to assist clinical decisions and de-escalate the current trend of over-diagnosis of skin cancer lesions as pathological.
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A superpixel-driven deep learning approach for the analysis of dermatological wounds
Gustavo Blanco,Agma J. M. Traina,Caetano Traina,Paulo Mazzoncini de Azevedo-Marques,Ana Elisa Serafim Jorge,Daniel de Oliveira,Marcos V. N. Bedo +6 more
TL;DR: Results indicate QTDU effectiveness for both tissue segmentation and wounded area quantification tasks, when compared to existing machine-learning approaches, the combination of superpixels and deep learning models outperformed the competitors within strong significant levels.
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Hybrid Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening.
TL;DR: A novel hybrid transfer learning technique is presented, in which a CNN is built and trained from scratch, with initial weights of only those filters which were identified as relevant using AlexNet and VGG-16 net to detect cervical cancer from cervix images.
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Shuang Zhao,Bin Xie,Y. Li,X. Zhao,Yehong Kuang,Juan Su,X. He,Xian Wu,Wei Fan,K. Huang,Yonghong Peng,Alexander A. Navarini,W. Huang,Xiang Chen +13 more
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Regional Detection of Traffic Congestion Using in a Large-Scale Surveillance System via Deep Residual TrafficNet
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References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
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Proceedings Article
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Proceedings Article
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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