Journal ArticleDOI
Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks
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TLDR
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
Citations
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Journal ArticleDOI
Attention Module Magnetic Flux Leakage Linked Deep Residual Network for Pipeline In-Line Inspection
Shucong Liu,Hongjun Wang,Rui Li +2 more
TL;DR: In this article , a pipeline MFL inspection signal identification method based on improved deep residual convolutional neural network and attention module is proposed to solve the issues of low identification efficiency, misjudgment and leakage judgment.
Journal ArticleDOI
Self-learning AI Framework for Skin Lesion Image Segmentation and Classification
TL;DR: Self-learning annotation scheme was proposed in the two-stage deep learning algorithm which consists of U-Net segmentation model with the annotation scheme and CNN classifier model for implementing the proposed self-learning Artificial Intelligence (AI) framework.
Journal ArticleDOI
Deep learning design for benign and malignant classification of skin lesions: a new approach
TL;DR: In this paper, ResNet50 and VGG-16 models are introduced with different strategies, with and without preprocessing and with or without Support Vector Machine (SVM), and both transfer learning and data augmentation are used to solve the problem of lack of tagged data.
Book ChapterDOI
Deep Learning in Medical Applications: Lesion Segmentation in Skin Cancer Images Using Modified and Improved Encoder-Decoder Architecture
TL;DR: In this paper, a modified and improved encoder-decoder architecture with a smaller network depth and a smaller number of kernels was proposed to enhance the segmentation process for skin cancer images.
Journal ArticleDOI
The incidence and risk of cutaneous toxicities associated with dabrafenib in melanoma patients: a systematic review and meta-analysis
Chen Peng,Lei Jie-Xin +1 more
TL;DR: The meta-analysis showed that the most frequent cutaneous adverse reactions from dabrafenib were rash, cSCC, alopecia, KA, HK and pruritus, and there was a significantly decreased risk of cS CC, alOPecia and HK with the combination of dabrafonib with trametinib versus dabrafanib alone.
References
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Proceedings ArticleDOI
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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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