791 research outputs found
Illumination-based Transformations Improve Skin Lesion Segmentation in Dermoscopic Images
The semantic segmentation of skin lesions is an important and common initial
task in the computer aided diagnosis of dermoscopic images. Although deep
learning-based approaches have considerably improved the segmentation accuracy,
there is still room for improvement by addressing the major challenges, such as
variations in lesion shape, size, color and varying levels of contrast. In this
work, we propose the first deep semantic segmentation framework for dermoscopic
images which incorporates, along with the original RGB images, information
extracted using the physics of skin illumination and imaging. In particular, we
incorporate information from specific color bands, illumination invariant
grayscale images, and shading-attenuated images. We evaluate our method on
three datasets: the ISBI ISIC 2017 Skin Lesion Segmentation Challenge dataset,
the DermoFit Image Library, and the PH2 dataset and observe improvements of
12.02%, 4.30%, and 8.86% respectively in the mean Jaccard index over a baseline
model trained only with RGB images.Comment: 10 pages, 5 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
(De)Constructing Bias on Skin Lesion Datasets
Melanoma is the deadliest form of skin cancer. Automated skin lesion analysis
plays an important role for early detection. Nowadays, the ISIC Archive and the
Atlas of Dermoscopy dataset are the most employed skin lesion sources to
benchmark deep-learning based tools. However, all datasets contain biases,
often unintentional, due to how they were acquired and annotated. Those biases
distort the performance of machine-learning models, creating spurious
correlations that the models can unfairly exploit, or, contrarily destroying
cogent correlations that the models could learn. In this paper, we propose a
set of experiments that reveal both types of biases, positive and negative, in
existing skin lesion datasets. Our results show that models can correctly
classify skin lesion images without clinically-meaningful information:
disturbingly, the machine-learning model learned over images where no
information about the lesion remains, presents an accuracy above the AI
benchmark curated with dermatologists' performances. That strongly suggests
spurious correlations guiding the models. We fed models with additional
clinically meaningful information, which failed to improve the results even
slightly, suggesting the destruction of cogent correlations. Our main findings
raise awareness of the limitations of models trained and evaluated in small
datasets such as the ones we evaluated, and may suggest future guidelines for
models intended for real-world deployment.Comment: 9 pages, 6 figures. Paper accepted at 2019 ISIC Skin Image Anaylsis
Workshop @ IEEE/CVF Conference on Computer Vision and Pattern Recognition
Workshops (CVPRW
Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features
The presence of certain clinical dermoscopic features within a skin lesion
may indicate melanoma, and automatically detecting these features may lead to
more quantitative and reproducible diagnoses. We reformulate the task of
classifying clinical dermoscopic features within superpixels as a segmentation
problem, and propose a fully convolutional neural network to detect clinical
dermoscopic features from dermoscopy skin lesion images. Our neural network
architecture uses interpolated feature maps from several intermediate network
layers, and addresses imbalanced labels by minimizing a negative multi-label
Dice-F score, where the score is computed across the mini-batch for each
label. Our approach ranked first place in the 2017 ISIC-ISBI Part 2:
Dermoscopic Feature Classification Task challenge over both the provided
validation and test datasets, achieving a 0.895% area under the receiver
operator characteristic curve score. We show how simple baseline models can
outrank state-of-the-art approaches when using the official metrics of the
challenge, and propose to use a fuzzy Jaccard Index that ignores the empty set
(i.e., masks devoid of positive pixels) when ranking models. Our results
suggest that (i) the classification of clinical dermoscopic features can be
effectively approached as a segmentation problem, and (ii) the current metrics
used to rank models may not well capture the efficacy of the model. We plan to
make our trained model and code publicly available.Comment: Accepted JBHI versio
Automatic Skin Lesion Segmentation using Semi-supervised Learning Technique
Skin cancer is the most common of all cancers and each year million cases of
skin cancer are treated. Treating and curing skin cancer is easy, if it is
diagnosed and treated at an early stage. In this work we propose an automatic
technique for skin lesion segmentation in dermoscopic images which helps in
classifying the skin cancer types. The proposed method comprises of two major
phases (1) preprocessing and (2) segmentation using semi-supervised learning
algorithm. In the preprocessing phase noise are removed using filtering
technique and in the segmentation phase skin lesions are segmented based on
clustering technique. K-means clustering algorithm is used to cluster the
preprocessed images and skin lesions are filtered from these clusters based on
the color feature. Color of the skin lesions are learned from the training
images using histograms calculations in RGB color space. The training images
were downloaded from the ISIC 2017 challenge website and the experimental
results were evaluated using validation and test sets.Comment: 4 pages with 1 figur
A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification
In this study, a multi-task deep neural network is proposed for skin lesion
analysis. The proposed multi-task learning model solves different tasks (e.g.,
lesion segmentation and two independent binary lesion classifications) at the
same time by exploiting commonalities and differences across tasks. This
results in improved learning efficiency and potential prediction accuracy for
the task-specific models, when compared to training the individual models
separately. The proposed multi-task deep learning model is trained and
evaluated on the dermoscopic image sets from the International Skin Imaging
Collaboration (ISIC) 2017 Challenge - Skin Lesion Analysis towards Melanoma
Detection, which consists of 2000 training samples and 150 evaluation samples.
The experimental results show that the proposed multi-task deep learning model
achieves promising performances on skin lesion segmentation and classification.
The average value of Jaccard index for lesion segmentation is 0.724, while the
average values of area under the receiver operating characteristic curve (AUC)
on two individual lesion classifications are 0.880 and 0.972, respectively.Comment: Submission to support ISIC 2017 challenge result
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
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
Detector-SegMentor Network for Skin Lesion Localization and Segmentation
Melanoma is a life-threatening form of skin cancer when left undiagnosed at
the early stages. Although there are more cases of non-melanoma cancer than
melanoma cancer, melanoma cancer is more deadly. Early detection of melanoma is
crucial for the timely diagnosis of melanoma cancer and prohibit its spread to
distant body parts. Segmentation of skin lesion is a crucial step in the
classification of melanoma cancer from the cancerous lesions in dermoscopic
images. Manual segmentation of dermoscopic skin images is very time consuming
and error-prone resulting in an urgent need for an intelligent and accurate
algorithm. In this study, we propose a simple yet novel network-in-network
convolution neural network(CNN) based approach for segmentation of the skin
lesion. A Faster Region-based CNN (Faster RCNN) is used for preprocessing to
predict bounding boxes of the lesions in the whole image which are subsequently
cropped and fed into the segmentation network to obtain the lesion mask. The
segmentation network is a combination of the UNet and Hourglass networks. We
trained and evaluated our models on ISIC 2018 dataset and also cross-validated
on PH\textsuperscript{2} and ISBI 2017 datasets. Our proposed method surpassed
the state-of-the-art with Dice Similarity Coefficient of 0.915 and Accuracy
0.959 on ISIC 2018 dataset and Dice Similarity Coefficient of 0.947 and
Accuracy 0.971 on ISBI 2017 dataset.Comment: 9 pages, 7 figures, accepted at NCVPRIPG 201
Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)
In this article, we describe the design and implementation of a publicly
accessible dermatology image analysis benchmark challenge. The goal of the
challenge is to sup- port research and development of algorithms for automated
diagnosis of melanoma, a lethal form of skin cancer, from dermoscopic images.
The challenge was divided into sub-challenges for each task involved in image
analysis, including lesion segmentation, dermoscopic feature detection within a
lesion, and classification of melanoma. Training data included 900 images. A
separate test dataset of 379 images was provided to measure resultant
performance of systems developed with the training data. Ground truth for both
training and test sets was generated by a panel of dermoscopic experts. In
total, there were 79 submissions from a group of 38 participants, making this
the largest standardized and comparative study for melanoma diagnosis in
dermoscopic images to date. While the official challenge duration and ranking
of participants has concluded, the datasets remain available for further
research and development
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