Dense deconvolution net: Multi path fusion and dense deconvolution for high resolution skin lesion segmentation

He, Xinzi; Yu, Zhen; Wang, Tianfu; Lei, Baiying; Shi, Yiyan

doi:10.3233/thc-174633

Cited by 30 publications

(15 citation statements)

References 13 publications

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“…The resulted metrics on ISIC2016 test set are reported in Table V, in which results from the other method in the literature and ISIC2016 original leaderboard submissions are reported too. Regarding V and based on JSI evaluation metric, from our proposed segmentation models, the Xception and ResNetV2 based segmentation networks are able to perform the same and even better than He et al method [43]. Considering the results from [43] as highest reported metrics in the literature, ResNetV2 based outperform it with a marginal difference of 0.1% and setting a new state-of-…”

Section: As You Can See In the Tablementioning

confidence: 63%

“…Regarding V and based on JSI evaluation metric, from our proposed segmentation models, the Xception and ResNetV2 based segmentation networks are able to perform the same and even better than He et al method [43]. Considering the results from [43] as highest reported metrics in the literature, ResNetV2 based outperform it with a marginal difference of 0.1% and setting a new state-of-…”

Section: As You Can See In the Tablementioning

confidence: 63%

“…Dense deconvolutional layers, chained residual poolings and auxiliary losses in two intermediate levels where utilized in the decoding path. He et al [43] proposed a multi-path deconvolutional neural network which contains residual, dense deconvolution (transposed convolution), and chained residual pooing blocks.…”

Section: Literature Reviewmentioning

confidence: 99%

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Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations

Jahanifar,

Tajeddin,

Koohbanani

et al. 2018

Preprint

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Computer-aided diagnosis systems for classification of different type of skin lesions have been an active field of research in recent decades. It has been shown that introducing lesions and their attributes masks into lesion classification pipeline can greatly improve the performance. In this paper, we propose a framework by incorporating transfer learning for segmenting lesions and their attributes based on the convolutional neural networks. The proposed framework is based on the encoderdecoder architecture which utilizes a variety of pre-trained networks in the encoding path and generates the prediction map by combining multi-scale information in decoding path using a pyramid pooling manner. To address the lack of training data and increase the proposed model generalization, an extensive set of novel domain-specific augmentation routines have been applied to simulate the real variations in dermoscopy images. Finally, by performing broad experiments on three different data sets obtained from International Skin Imaging Collaboration archive (ISIC2016, ISIC2017, and ISIC2018 challenges data sets), we show that the proposed method outperforms other state-of-the-art approaches for ISIC2016 and ISIC2017 segmentation task and achieved the 1 st rank on the leader-board of ISIC2018 attribute detection task.

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Section: As You Can See In the Tablementioning

confidence: 63%

Section: As You Can See In the Tablementioning

confidence: 63%

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Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations

Jahanifar,

Tajeddin,

Koohbanani

et al. 2018

Preprint

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“…The system was evaluated using two publicly available databases,ISBI 2017 Challenge and PH2 datasets. He et al [44] presented a skin lesion segmentation network using a very deep dense deconvolution network. They employed the combination of deep dense layer and generic multi-path Deep RefineNet.…”

Section: B Automatic Deep Learning Techniquesmentioning

confidence: 99%

Deep Learning-Based System for Automatic Melanoma Detection

2020

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Melanoma is the deadliest form of skin cancer. Distinguishing melanoma lesions from nonmelanoma lesions has however been a challenging task. Many Computer Aided Diagnosis and Detection Systems have been developed in the past for this task. They have been limited in performance due to the complex visual characteristics of the skin lesion images which consists of inhomogeneous features and fuzzy boundaries. In this paper, we propose a deep learning-based method that overcomes these limitations for automatic melanoma lesion detection and segmentation. An enhanced encoder-decoder network with encoder and decoder sub-networks connected through a series of skip pathways which brings the semantic level of the encoder feature maps closer to that of the decoder feature maps is proposed for efficient learning and feature extraction. The system employs multi-stage and multi-scale approach and utilizes softmax classifier for pixel-wise classification of melanoma lesions. We devise a new method called Lesion-classifier that performs the classification of skin lesions into melanoma and non-melanoma based on results derived from pixel-wise classification. Our experiments on two well-established public benchmark skin lesion datasets, International Symposium on Biomedical Imaging(ISBI)2017 and Hospital Pedro Hispano (PH2), demonstrate that our method is more effective than some state-of-the-art methods. We achieved accuracy and dice coefficient of 95% and 92% on ISIC 2017 dataset and accuracy and dice coefficient of 95% and 93% on PH2 datasets.

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“…However, the accuracy of the methods for skin lesion segmentation, especially for low-density regions of skin lesions is not high. The skin lesion segmentation methods based on dense deconvolution networks [11,12] were proposed. Although these methods are good enough for skin lesion segmentation, they cannot reliably segment low-intensity regions.…”

Section: Introductionmentioning

confidence: 99%

Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation

Thanh

Hai²,

Hieu

et al. 2021

Computer Optics

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Melanoma skin cancer is one of the most dangerous forms of skin cancer because it grows fast and causes most of the skin cancer deaths. Hence, early detection is a very important task to treat melanoma. In this article, we propose a skin lesion segmentation method for dermoscopic images based on the U-Net architecture with VGG-16 encoder and the semantic segmentation. Base on the segmented skin lesion, diagnostic imaging systems can evaluate skin lesion features to classify them. The proposed method requires fewer resources for training, and it is suitable for computing systems without powerful GPUs, but the training accuracy is still high enough (above 95 %). In the experiments, we train the model on the ISIC dataset – a common dermoscopic image dataset. To assess the performance of the proposed skin lesion segmentation method, we evaluate the Sorensen-Dice and the Jaccard scores and compare to other deep learning-based skin lesion segmentation methods. Experimental results showed that skin lesion segmentation quality of the proposed method are better than ones of the compared methods.

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Dense deconvolution net: Multi path fusion and dense deconvolution for high resolution skin lesion segmentation

Cited by 30 publications

References 13 publications

Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations

Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations

Deep Learning-Based System for Automatic Melanoma Detection

Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation

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