Dermatologist-level classification of skin cancer（这可是nature呀）1
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2 FEBRUARY 2017 | VOL 542 | NATURE | 115
LETTER
doi:10.1038/nature21056
Dermatologist-level classification of skin cancer
with deep neural networks
Andre Esteva
1
*, Brett Kuprel
1
*, Roberto A. Novoa
2,3
, Justin Ko
2
, Susan M. Swetter
2,4
, Helen M. Blau
5
& Sebastian Thrun
6
Skin cancer, the most common human malignancy
1–3
, is primarily
diagnosed visually, beginning with an initial clinical screening
and followed potentially by dermoscopic analysis, a biopsy and
histopathological examination. Automated classification of skin
lesions using images is a challenging task owing to the fine-grained
variability in the appearance of skin lesions. Deep convolutional
neural networks (CNNs)
4,5
show potential for general and highly
variable tasks across many fine-grained object categories
6–11
.
Here we demonstrate classification of skin lesions using a single
CNN, trained end-to-end from images directly, using only pixels
and disease labels as inputs. We train a CNN using a dataset of
129,450clinical images—two orders of magnitude larger than
previous datasets
12
—consisting of 2,032different diseases. We
test its performance against 21board-certified dermatologists on
biopsy-proven clinical images with two critical binary classification
use cases: keratinocyte carcinomas versus benign seborrheic
keratoses; and malignant melanomas versus benign nevi. The first
case represents the identification of the most common cancers, the
second represents the identification of the deadliest skin cancer.
The CNN achieves performance on par with all tested experts
across both tasks, demonstrating an artificial intelligence capable
of classifying skin cancer with a level of competence comparable to
dermatologists. Outfitted with deep neural networks, mobile devices
can potentially extend the reach of dermatologists outside of the
clinic. It is projected that 6.3billion smartphone subscriptions will
exist by the year 2021 (ref. 13) and can therefore potentially provide
low-cost universal access to vital diagnostic care.
There are 5.4million new cases of skin cancer in the United States
2
every year. One in five Americans will be diagnosed with a cutaneous
malignancy in their lifetime. Although melanomas represent fewer than
5% of all skin cancers in the United States, they account for approxi-
mately 75% of all skin-cancer-related deaths, and are responsible for
over 10,000deaths annually in the United States alone. Early detection
is critical, as the estimated 5-year survival rate for melanoma drops
from over 99% if detected in its earliest stages to about 14% if detected
in its latest stages. We developed a computational method which may
allow medical practitioners and patients to proactively track skin
lesions and detect cancer earlier. By creating a novel disease taxonomy,
and a disease-partitioning algorithm that maps individual diseases into
training classes, we are able to build a deep learning system for auto-
mated dermatology.
Previous work in dermatological computer-aided classification
12,14,15
has lacked the generalization capability of medical practitioners
owing to insufficient data and a focus on standardized tasks such as
dermoscopy
16–18
and histological image classification
19–22
. Dermoscopy
images are acquired via a specialized instrument and histological
images are acquired via invasive biopsy and microscopy; whereby
both modalities yield highly standardized images. Photographic
images (for example, smartphone images) exhibit variability in factors
such as zoom, angle and lighting, making classification substantially
more challenging
23,24
. We overcome this challenge by using a data-
driven approach—1.41million pre-training and training images
make classification robust to photographic variability. Many previous
techniques require extensive preprocessing, lesion segmentation and
extraction of domain-specific visual features before classification. By
contrast, our system requires no hand-crafted features; it is trained
end-to-end directly from image labels and raw pixels, with a single
network for both photographic and dermoscopic images. The existing
body of work uses small datasets of typically less than a thousand
images of skin lesions
16,18,19
, which, as a result, do not generalize well
to new images. We demonstrate generalizable classification with a new
dermatologist-labelled dataset of 129,450clinical images, including
3,374dermoscopy images.
Deep learning algorithms, powered by advances in computation
and very large datasets
25
, have recently been shown to exceed human
performance in visual tasks such as playing Atari games
26
, strategic
board games like Go
27
and object recognition
6
. In this paper we
outline the development of a CNN that matches the performance of
dermatologists at three key diagnostic tasks: melanoma classification,
melanoma classification using dermoscopy and carcinoma
classification. We restrict the comparisons to image-based classification.
We utilize a GoogleNet Inception v3 CNN architecture
9
that was pre-
trained on approximately 1.28million images (1,000object categories)
from the 2014 ImageNet Large Scale Visual Recognition Challenge
6
,
and train it on our dataset using transfer learning
28
. Figure 1 shows the
working system. The CNN is trained using 757disease classes. Our
dataset is composed of dermatologist-labelled images organized in a
tree-structured taxonomy of 2,032diseases, in which the individual
diseases form the leaf nodes. The images come from 18different
clinician-curated, open-access online repositories, as well as from
clinical data from Stanford University Medical Center. Figure 2a shows
a subset of the full taxonomy, which has been organized clinically and
visually by medical experts. We split our dataset into 127,463training
and validation images and 1,942biopsy-labelled test images.
To take advantage of fine-grained information contained within the
taxonomy structure, we develop an algorithm (Extended Data Table 1)
to partition diseases into fine-grained training classes (for example,
amelanotic melanoma and acrolentiginous melanoma). During
inference, the CNN outputs a probability distribution over these fine
classes. To recover the probabilities for coarser-level classes of interest
(for example, melanoma) we sum the probabilities of their descendants
(see Methods and Extended Data Fig. 1 for more details).
We validate the effectiveness of the algorithm in two ways, using
nine-fold cross-validation. First, we validate the algorithm using a
three-class disease partition—the first-level nodes of the taxonomy,
which represent benign lesions, malignant lesions and non-neoplastic
1
Department of Electrical Engineering, Stanford University, Stanford, California, USA.
2
Department of Dermatology, Stanford University, Stanford, California, USA.
3
Department of Pathology,
Stanford University, Stanford, California, USA.
4
Dermatology Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA.
5
Baxter Laboratory for Stem Cell Biology, Department
of Microbiology and Immunology, Institute for Stem CellBiology and RegenerativeMedicine, Stanford University, Stanford, California, USA.
6
Department of Computer Science, Stanford University,
Stanford, California, USA.
*These authors contributed equally to this work.
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
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