16,589 research outputs found
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
(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
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
Classification of Dermoscopy Images using Deep Learning
Skin cancer is one of the most common forms of cancer and its incidence is
projected to rise over the next decade. Artificial intelligence is a viable
solution to the issue of providing quality care to patients in areas lacking
access to trained dermatologists. Considerable progress has been made in the
use of automated applications for accurate classification of skin lesions from
digital images. In this manuscript, we discuss the design and implementation of
a deep learning algorithm for classification of dermoscopy images from the
HAM10000 Dataset. We trained a convolutional neural network based on the
ResNet50 architecture to accurately classify dermoscopy images of skin lesions
into one of seven disease categories. Using our custom model, we obtained a
balanced accuracy of 91% on the validation dataset
Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks
Melanoma is clinically difficult to distinguish from common benign skin
lesions, particularly melanocytic naevus and seborrhoeic keratosis. The
dermoscopic appearance of these lesions has huge intra-class variations and
high inter-class visual similarities. Most current research is focusing on
single-class segmentation irrespective of classes of skin lesions. In this
work, we evaluate the performance of deep learning on multi-class segmentation
of ISIC-2017 challenge dataset, which consists of 2,750 dermoscopic images. We
propose an end-to-end solution using fully convolutional networks (FCNs) for
multi-class semantic segmentation to automatically segment the melanoma,
seborrhoeic keratosis and naevus. To improve the performance of FCNs, transfer
learning and a hybrid loss function are used. We evaluate the performance of
the deep learning segmentation methods for multi-class segmentation and lesion
diagnosis (with post-processing method) on the testing set of the ISIC-2017
challenge dataset. The results showed that the two-tier level transfer learning
FCN-8s achieved the overall best result with \textit{Dice} score of 78.5% in a
naevus category, 65.3% in melanoma, and 55.7% in seborrhoeic keratosis in
multi-class segmentation and Accuracy of 84.62% for recognition of melanoma in
lesion diagnosis.Comment: Comp2clinic workshop at Biostec 202
Deep Learning for Skin Lesion Classification
Melanoma, a malignant form of skin cancer is very threatening to life.
Diagnosis of melanoma at an earlier stage is highly needed as it has a very
high cure rate. Benign and malignant forms of skin cancer can be detected by
analyzing the lesions present on the surface of the skin using dermoscopic
images. In this work, an automated skin lesion detection system has been
developed which learns the representation of the image using Google's
pretrained CNN model known as Inception-v3 \cite{cnn}. After obtaining the
representation vector for our input dermoscopic images we have trained two
layer feed forward neural network to classify the images as malignant or
benign. The system also classifies the images based on the cause of the cancer
either due to melanocytic or non-melanocytic cells using a different neural
network. These classification tasks are part of the challenge organized by
International Skin Imaging Collaboration (ISIC) 2017. Our system learns to
classify the images based on the model built using the training images given in
the challenge and the experimental results were evaluated using validation and
test sets. Our system has achieved an overall accuracy of 65.8\% for the
validation set.Comment: 3 pages with 3 figure
Multiple Abnormality Detection for Automatic Medical Image Diagnosis Using Bifurcated Convolutional Neural Network
Automating classification and segmentation process of abnormal regions in
different body organs has a crucial role in most of medical imaging
applications such as funduscopy, endoscopy, and dermoscopy. Detecting multiple
abnormalities in each type of images is necessary for better and more accurate
diagnosis procedure and medical decisions. In recent years portable medical
imaging devices such as capsule endoscopy and digital dermatoscope have been
introduced and made the diagnosis procedure easier and more efficient. However,
these portable devices have constrained power resources and limited
computational capability. To address this problem, we propose a bifurcated
structure for convolutional neural networks performing both classification and
segmentation of multiple abnormalities simultaneously. The proposed network is
first trained by each abnormality separately. Then the network is trained using
all abnormalities. In order to reduce the computational complexity, the network
is redesigned to share some features which are common among all abnormalities.
Later, these shared features are used in different settings (directions) to
segment and classify the abnormal region of the image. Finally, results of the
classification and segmentation directions are fused to obtain the classified
segmentation map. Proposed framework is simulated using four frequent
gastrointestinal abnormalities as well as three dermoscopic lesions and for
evaluation of the proposed framework the results are compared with the
corresponding ground truth map. Properties of the bifurcated network like low
complexity and resource sharing make it suitable to be implemented as a part of
portable medical imaging devices
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 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
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
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