3,357 research outputs found
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
What evidence does deep learning model use to classify Skin Lesions?
Melanoma is a type of skin cancer with the most rapidly increasing incidence.
Early detection of melanoma using dermoscopy images significantly increases
patients' survival rate. However, accurately classifying skin lesions by eye,
especially in the early stage of melanoma, is extremely challenging for the
dermatologists. Hence, the discovery of reliable biomarkers will be meaningful
for melanoma diagnosis. Recent years, the value of deep learning empowered
computer-assisted diagnose has been shown in biomedical imaging based decision
making. However, much research focuses on improving disease detection accuracy
but not exploring the evidence of pathology. In this paper, we propose a method
to interpret the deep learning classification findings. Firstly, we propose an
accurate neural network architecture to classify skin lesions. Secondly, we
utilize a prediction difference analysis method that examines each patch on the
image through patch-wised corrupting to detect the biomarkers. Lastly, we
validate that our biomarker findings are corresponding to the patterns in the
literature. The findings can be significant and useful to guide clinical
diagnosis.Comment: 5 page
Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks
Malignant melanoma has one of the most rapidly increasing incidences in the
world and has a considerable mortality rate. Early diagnosis is particularly
important since melanoma can be cured with prompt excision. Dermoscopy images
play an important role in the non-invasive early detection of melanoma [1].
However, melanoma detection using human vision alone can be subjective,
inaccurate and poorly reproducible even among experienced dermatologists. This
is attributed to the challenges in interpreting images with diverse
characteristics including lesions of varying sizes and shapes, lesions that may
have fuzzy boundaries, different skin colors and the presence of hair [2].
Therefore, the automatic analysis of dermoscopy images is a valuable aid for
clinical decision making and for image-based diagnosis to identify diseases
such as melanoma [1-4]. Deep residual networks (ResNets) has achieved
state-of-the-art results in image classification and detection related problems
[5-8]. In this ISIC 2017 skin lesion analysis challenge [9], we propose to
exploit the deep ResNets for robust visual features learning and
representations.Comment: Submission for ISIC2017 Challeng
Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification
Convolutional neural networks (CNNs) deliver exceptional results for computer
vision, including medical image analysis. With the growing number of available
architectures, picking one over another is far from obvious. Existing art
suggests that, when performing transfer learning, the performance of CNN
architectures on ImageNet correlates strongly with their performance on target
tasks. We evaluate that claim for melanoma classification, over 9 CNNs
architectures, in 5 sets of splits created on the ISIC Challenge 2017 dataset,
and 3 repeated measures, resulting in 135 models. The correlations we found
were, to begin with, much smaller than those reported by existing art, and
disappeared altogether when we considered only the top-performing networks:
uncontrolled nuisances (i.e., splits and randomness) overcome any of the
analyzed factors. Whenever possible, the best approach for melanoma
classification is still to create ensembles of multiple models. We compared two
choices for selecting which models to ensemble: picking them at random (among a
pool of high-quality ones) vs. using the validation set to determine which ones
to pick first. For small ensembles, we found a slight advantage on the second
approach but found that random choice was also competitive. Although our aim in
this paper was not to maximize performance, we easily reached AUCs comparable
to the first place on the ISIC Challenge 2017.Comment: ISIC Skin Image Analysis Workshop @ CVPR 201
Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks
We present a deep learning approach to the ISIC 2017 Skin Lesion
Classification Challenge using a multi-scale convolutional neural network. Our
approach utilizes an Inception-v3 network pre-trained on the ImageNet dataset,
which is fine-tuned for skin lesion classification using two different scales
of input images
An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning
The incidence of malignant melanoma continues to increase worldwide. This
cancer can strike at any age; it is one of the leading causes of loss of life
in young persons. Since this cancer is visible on the skin, it is potentially
detectable at a very early stage when it is curable. New developments have
converged to make fully automatic early melanoma detection a real possibility.
First, the advent of dermoscopy has enabled a dramatic boost in clinical
diagnostic ability to the point that melanoma can be detected in the clinic at
the very earliest stages. The global adoption of this technology has allowed
accumulation of large collections of dermoscopy images of melanomas and benign
lesions validated by histopathology. The development of advanced technologies
in the areas of image processing and machine learning have given us the ability
to allow distinction of malignant melanoma from the many benign mimics that
require no biopsy. These new technologies should allow not only earlier
detection of melanoma, but also reduction of the large number of needless and
costly biopsy procedures. Although some of the new systems reported for these
technologies have shown promise in preliminary trials, widespread
implementation must await further technical progress in accuracy and
reproducibility. In this paper, we provide an overview of computerized
detection of melanoma in dermoscopy images. First, we discuss the various
aspects of lesion segmentation. Then, we provide a brief overview of clinical
feature segmentation. Finally, we discuss the classification stage where
machine learning algorithms are applied to the attributes generated from the
segmented features to predict the existence of melanoma.Comment: 15 pages, 3 figure
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
Dynamic recursive tree-based partitioning for malignant melanoma identification in skin lesion dermoscopic images
In this paper, multivalued data or multiple values variables are defined.
They are typical when there is some intrinsic uncertainty in data production,
as the result of imprecise measuring instruments, such as in image recognition,
in human judgments and so on. \noindent So far, contributions in symbolic data
analysis literature provide data preprocessing criteria allowing for the use of
standard methods such as factorial analysis, clustering, discriminant analysis,
tree-based methods. As an alternative, this paper introduces a methodology for
supervised classification, the so-called Dynamic CLASSification TREE (D-CLASS
TREE), dealing simultaneously with both standard and multivalued data as well.
For that, an innovative partitioning criterion with a tree-growing algorithm
will be defined. Main result is a dynamic tree structure characterized by the
simultaneous presence of binary and ternary partitions. A real world case study
will be considered to show the advantages of the proposed methodology and main
issues of the interpretation of the final results. A comparative study with
other approaches dealing with the same types of data will be also shown.
D-CLASS TREE outperforms its competitors in terms of accuracy, which is a
fundamental aspect for predictive learning
Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods
Dermoscopy is one of the major imaging modalities used in the diagnosis of
melanoma and other pigmented skin lesions. Due to the difficulty and
subjectivity of human interpretation, automated analysis of dermoscopy images
has become an important research area. Border detection is often the first step
in this analysis. In many cases, the lesion can be roughly separated from the
background skin using a thresholding method applied to the blue channel.
However, no single thresholding method appears to be robust enough to
successfully handle the wide variety of dermoscopy images encountered in
clinical practice. In this paper, we present an automated method for detecting
lesion borders in dermoscopy images using ensembles of thresholding methods.
Experiments on a difficult set of 90 images demonstrate that the proposed
method is robust, fast, and accurate when compared to nine state-of-the-art
methods.Comment: 8 pages, 3 figures, 2 tables. arXiv admin note: substantial text
overlap with arXiv:1009.136
A Deep Multi-task Learning Approach to Skin Lesion Classification
Skin lesion identification is a key step toward dermatological diagnosis.
When describing a skin lesion, it is very important to note its body site
distribution as many skin diseases commonly affect particular parts of the
body. To exploit the correlation between skin lesions and their body site
distributions, in this study, we investigate the possibility of improving skin
lesion classification using the additional context information provided by body
location. Specifically, we build a deep multi-task learning (MTL) framework to
jointly optimize skin lesion classification and body location classification
(the latter is used as an inductive bias). Our MTL framework uses the
state-of-the-art ImageNet pretrained model with specialized loss functions for
the two related tasks. Our experiments show that the proposed MTL based method
performs more robustly than its standalone (single-task) counterpart.Comment: AAAI 2017 Joint Workshop on Health Intelligence W3PHIAI 2017 (W3PHI &
HIAI), San Francisco, CA, 201
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