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
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Book ChapterDOI
Skin Lesion Classification Using Convolutional Neural Network for Melanoma Recognition
TL;DR: This article proposes a robust and automatic framework for the skin lesion classification (SLC), where it has integrated image augmentation, deep convolutional neural network (DCNN), and transfer learning and can successfully distinguish skin cancer with a high degree of accuracy.
Proceedings ArticleDOI
From Deep Learning Towards Finding Skin Lesion Biomarkers
TL;DR: Surprisingly, surround skins also can be used as evidence for skin lesion diagnosis, which has not been included in traditional diagnosis rules, and the biomarkers discovered from deep learning classifier can be significant and useful to guide clinical diagnosis.
Proceedings ArticleDOI
Performance analysis of Convolutional Neural Network (CNN) based Cancerous Skin Lesion Detection System
TL;DR: A Batch Normalized Convolutional Neural Network (BN-CNN) is proposed, which consists of 6 layers of convolutional blocks with batch normalization followed by a fully connected layer that performs binary classification of dermoscopic images.
Book ChapterDOI
Webly Supervised Learning for Skin Lesion Classification
TL;DR: In this paper, a two-step transfer learning based training process with a robust loss function is proposed to train deep models for the task of fine-grained skin lesion classification.
Journal ArticleDOI
Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks.
TL;DR: In this article, the authors investigated sixty different architectures of the feedforward back propagation network (FFBPN), based on shape asymmetry for an optimal structural design that includes both the hidden neuron number and the input data selection.
References
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Proceedings Article
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Karen Simonyan,Andrew Zisserman +1 more
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