120 research outputs found
Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations
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 encoder-decoder 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 first rank on the leader-board of ISIC2018 attribute detection
task.Comment: 18 page
Automatic Lesion Boundary Segmentation in Dermoscopic Images with Ensemble Deep Learning Methods
Early detection of skin cancer, particularly melanoma, is crucial to enable
advanced treatment. Due to the rapid growth in the numbers of skin cancers,
there is a growing need of computerized analysis for skin lesions. The
state-of-the-art public available datasets for skin lesions are often
accompanied with very limited amount of segmentation ground truth labeling as
it is laborious and expensive. The lesion boundary segmentation is vital to
locate the lesion accurately in dermoscopic images and lesion diagnosis of
different skin lesion types. In this work, we propose the use of fully
automated deep learning ensemble methods for accurate lesion boundary
segmentation in dermoscopic images. We trained the Mask-RCNN and DeepLabv3+
methods on ISIC-2017 segmentation training set and evaluate the performance of
the ensemble networks on ISIC-2017 testing set. Our results showed that the
best proposed ensemble method segmented the skin lesions with Jaccard index of
79.58% for the ISIC-2017 testing set. The proposed ensemble method outperformed
FrCN, FCN, U-Net, and SegNet in Jaccard Index by 2.48%, 7.42%, 17.95%, and
9.96% respectively. Furthermore, the proposed ensemble method achieved an
accuracy of 95.6% for some representative clinically benign cases, 90.78% for
the melanoma cases, and 91.29% for the seborrheic keratosis cases on ISIC-2017
testing set, exhibiting better performance than FrCN, FCN, U-Net, and SegNet.Comment: 7 pages, 8 figures and 4 tables. arXiv admin note: text overlap with
arXiv:1711.1044
Generative Adversarial Network in Medical Imaging: A Review
Generative adversarial networks have gained a lot of attention in the
computer vision community due to their capability of data generation without
explicitly modelling the probability density function. The adversarial loss
brought by the discriminator provides a clever way of incorporating unlabeled
samples into training and imposing higher order consistency. This has proven to
be useful in many cases, such as domain adaptation, data augmentation, and
image-to-image translation. These properties have attracted researchers in the
medical imaging community, and we have seen rapid adoption in many traditional
and novel applications, such as image reconstruction, segmentation, detection,
classification, and cross-modality synthesis. Based on our observations, this
trend will continue and we therefore conducted a review of recent advances in
medical imaging using the adversarial training scheme with the hope of
benefiting researchers interested in this technique.Comment: 24 pages; v4; added missing references from before Jan 1st 2019;
accepted to MedI
Skin disease diagnosis with deep learning: a review
Skin cancer is one of the most threatening diseases worldwide. However,
diagnosing skin cancer correctly is challenging. Recently, deep learning
algorithms have emerged to achieve excellent performance on various tasks.
Particularly, they have been applied to the skin disease diagnosis tasks. In
this paper, we present a review on deep learning methods and their applications
in skin disease diagnosis. We first present a brief introduction to skin
diseases and image acquisition methods in dermatology, and list several
publicly available skin datasets for training and testing algorithms. Then, we
introduce the conception of deep learning and review popular deep learning
architectures. Thereafter, popular deep learning frameworks facilitating the
implementation of deep learning algorithms and performance evaluation metrics
are presented. As an important part of this article, we then review the
literature involving deep learning methods for skin disease diagnosis from
several aspects according to the specific tasks. Additionally, we discuss the
challenges faced in the area and suggest possible future research directions.
The major purpose of this article is to provide a conceptual and systematically
review of the recent works on skin disease diagnosis with deep learning. Given
the popularity of deep learning, there remains great challenges in the area, as
well as opportunities that we can explore in the future
Skin Lesion Segmentation in Dermoscopic Images with Noisy Data
We Propose a Deep Learning Approach to Segment the Skin Lesion in Dermoscopic Images. the Proposed Network Architecture Uses a Pretrained Efficient Net Model in the Encoder and Squeeze-And-Excitation Residual Structures in the Decoder. We Applied This Approach on the Publicly Available International Skin Imaging Collaboration (ISIC) 2017 Challenge Skin Lesion Segmentation Dataset. This Benchmark Dataset Has Been Widely Used in Previous Studies. We Observed Many Inaccurate or Noisy Ground Truth Labels. to Reduce Noisy Data, We Manually Sorted All Ground Truth Labels into Three Categories — Good, Mildly Noisy, and Noisy Labels. Furthermore, We Investigated the Effect of Such Noisy Labels in Training and Test Sets. Our Test Results Show that the Proposed Method Achieved Jaccard Scores of 0.807 on the Official ISIC 2017 Test Set and 0.832 on the Curated ISIC 2017 Test Set, Exhibiting Better Performance Than Previously Reported Methods. Furthermore, the Experimental Results Showed that the Noisy Labels in the Training Set Did Not Lower the Segmentation Performance. However, the Noisy Labels in the Test Set Adversely Affected the Evaluation Scores. We Recommend that the Noisy Labels Should Be Avoided in the Test Set in Future Studies for Accurate Evaluation of the Segmentation Algorithms
A Multi-stage Framework with Context Information Fusion Structure for Skin Lesion Segmentation
The computer-aided diagnosis (CAD) systems can highly improve the reliability
and efficiency of melanoma recognition. As a crucial step of CAD, skin lesion
segmentation has the unsatisfactory accuracy in existing methods due to large
variability in lesion appearance and artifacts. In this work, we propose a
framework employing multi-stage UNets (MS-UNet) in the auto-context scheme to
segment skin lesion accurately end-to-end. We apply two approaches to boost the
performance of MS-UNet. First, UNet is coupled with a context information
fusion structure (CIFS) to integrate the low-level and context information in
the multi-scale feature space. Second, to alleviate the gradient vanishing
problem, we use deep supervision mechanism through supervising MS-UNet by
minimizing a weighted Jaccard distance loss function. Four out of five commonly
used performance metrics, including Jaccard index and Dice coefficient, show
that our approach outperforms the state-ofthe-art deep learning based methods
on the ISBI 2016 Skin Lesion Challenge dataset.Comment: 4 pages, 3 figures, 1 tabl
Supervised Versus Unsupervised Deep Learning Based Methods for Skin Lesion Segmentation in Dermoscopy Images
Image segmentation is considered a crucial step in automatic dermoscopic image analysis as it affects the accuracy of subsequent steps. The huge progress in deep learning has recently revolutionized the image recognition and computer vision domains. In this paper, we compare a supervised deep learning based approach with an unsupervised deep learning based approach for the task of skin lesion segmentation in dermoscopy images. Results show that, by using the default parameter settings and network configurations proposed in the original approaches, although the unsupervised approach could detect fine structures of skin lesions in some occasions, the supervised approach shows much higher accuracy in terms of Dice coefficient and Jaccard index compared to the unsupervised approach, resulting in 77.7% vs. 40% and 67.2% vs. 30.4%, respectively. With a proposed modification to the unsupervised approach, the Dice and Jaccard values improved to 54.3% and 44%, respectively
DSNet: Automatic Dermoscopic Skin Lesion Segmentation
Automatic segmentation of skin lesion is considered a crucial step in
Computer Aided Diagnosis (CAD) for melanoma diagnosis. Despite its
significance, skin lesion segmentation remains a challenging task due to their
diverse color, texture, and indistinguishable boundaries and forms an open
problem. Through this study, we present a new and automatic semantic
segmentation network for robust skin lesion segmentation named Dermoscopic Skin
Network (DSNet). In order to reduce the number of parameters to make the
network lightweight, we used depth-wise separable convolution in lieu of
standard convolution to project the learned discriminating features onto the
pixel space at different stages of the encoder. Additionally, we implemented
U-Net and Fully Convolutional Network (FCN8s) to compare against the proposed
DSNet. We evaluate our proposed model on two publicly available datasets,
namely ISIC-2017 and PH2. The obtained mean Intersection over Union (mIoU) is
77.5 % and 87.0 % respectively for ISIC-2017 and PH2 datasets which
outperformed the ISIC-2017 challenge winner by 1.0 % with respect to mIoU. Our
proposed network also outperformed U-Net and FCN8s respectively by 3.6 % and
6.8 % with respect to mIoU on the ISIC-2017 dataset. Our network for skin
lesion segmentation outperforms other methods and can provide better segmented
masks on two different test datasets which can lead to better performance in
melanoma detection. Our trained model along with the source code and predicted
masks are made publicly available.Comment: 25 page
A Review on Skin Disease Classification and Detection Using Deep Learning Techniques
Skin cancer ranks among the most dangerous cancers. Skin cancers are commonly referred to as Melanoma. Melanoma is brought on by genetic faults or mutations on the skin, which are caused by Unrepaired Deoxyribonucleic Acid (DNA) in skin cells. It is essential to detect skin cancer in its infancy phase since it is more curable in its initial phases. Skin cancer typically progresses to other regions of the body. Owing to the disease's increased frequency, high mortality rate, and prohibitively high cost of medical treatments, early diagnosis of skin cancer signs is crucial. Due to the fact that how hazardous these disorders are, scholars have developed a number of early-detection techniques for melanoma. Lesion characteristics such as symmetry, colour, size, shape, and others are often utilised to detect skin cancer and distinguish benign skin cancer from melanoma. An in-depth investigation of deep learning techniques for melanoma's early detection is provided in this study. This study discusses the traditional feature extraction-based machine learning approaches for the segmentation and classification of skin lesions. Comparison-oriented research has been conducted to demonstrate the significance of various deep learning-based segmentation and classification approaches
Step-wise Integration of Deep Class-specific Learning for Dermoscopic Image Segmentation
The segmentation of abnormal regions on dermoscopic images is an important step for automated computer aided diagnosis (CAD) of skin lesions. Recent methods based on fully convolutional networks (FCN) have been very successful for dermoscopic image segmentation. However, they tend to overfit to the visual characteristics that are present in the dominant non-melanoma studies and therefore, perform poorly on the complex visual characteristics exhibited by melanoma studies, which usually consists of fuzzy boundaries and heterogeneous textures. In this paper, we propose a new method for automated skin lesion segmentation that overcomes these limitations via a novel deep class-specific learning approach which learns the important visual characteristics of the skin lesions of each individual class (melanoma vs non-melanoma) on an individual basis. We also introduce a new probability-based, step-wise integration to combine complementary segmentation results derived from individual class-specific learning models. We achieved an average Dice coefficient of 85.66% on the ISBI 2017 Skin Lesion Challenge (SLC), 91.77% on the ISBI 2016 SLC and 92.10% on the PH2 datasets with corresponding Jaccard indices of 77.73%, 85.92% and 85.90%, respectively, for the same datasets. Our experiments on three well-established public benchmark datasets demonstrate that our method is more effective than other state-of-the-art methods for skin lesion segmentation
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