Nav: Home

Deep learning algorithm does as well as dermatologists in identifying skin cancer

January 25, 2017

It's scary enough making a doctor's appointment to see if a strange mole could be cancerous. Imagine, then, that you were in that situation while also living far away from the nearest doctor, unable to take time off work and unsure you had the money to cover the cost of the visit. In a scenario like this, an option to receive a diagnosis through your smartphone could be lifesaving.

Universal access to health care was on the minds of computer scientists at Stanford when they set out to create an artificially intelligent diagnosis algorithm for skin cancer. They made a database of nearly 130,000 skin disease images and trained their algorithm to visually diagnose potential cancer. From the very first test, it performed with inspiring accuracy.

"We realized it was feasible, not just to do something well, but as well as a human dermatologist," said Sebastian Thrun, an adjunct professor in the Stanford Artificial Intelligence Laboratory. "That's when our thinking changed. That's when we said, 'Look, this is not just a class project for students, this is an opportunity to do something great for humanity.'"

The final product, the subject of a paper in the Jan. 25 issue of Nature, was tested against 21 board-certified dermatologists. In its diagnoses of skin lesions, which represented the most common and deadliest skin cancers, the algorithm matched the performance of dermatologists.

Why skin cancer

Every year there are about 5.4 million new cases of skin cancer in the United States, and while the five-year survival rate for melanoma detected in its earliest states is around 97 percent, that drops to approximately 14 percent if it's detected in its latest stages. Early detection could likely have an enormous impact on skin cancer outcomes.

Diagnosing skin cancer begins with a visual examination. A dermatologist usually looks at the suspicious lesion with the naked eye and with the aid of a dermatoscope, which is a handheld microscope that provides low-level magnification of the skin. If these methods are inconclusive or lead the dermatologist to believe the lesion is cancerous, a biopsy is the next step.

Bringing this algorithm into the examination process follows a trend in computing that combines visual processing with deep learning, a type of artificial intelligence modeled after neural networks in the brain. Deep learning has a decades-long history in computer science but it only recently has been applied to visual processing tasks, with great success. The essence of machine learning, including deep learning, is that a computer is trained to figure out a problem rather than having the answers programmed into it.

"We made a very powerful machine learning algorithm that learns from data," said Andre Esteva, co-lead author of the paper and a graduate student in the Thrun lab. "Instead of writing into computer code exactly what to look for, you let the algorithm figure it out."

The algorithm was fed each image as raw pixels with an associated disease label. Compared to other methods for training algorithms, this one requires very little processing or sorting of the images prior to classification, allowing the algorithm to work off a wider variety of data.

From cats and dogs to melanomas and carcinomas

Rather than building an algorithm from scratch, the researchers began with an algorithm developed by Google that was already trained to identify 1.28 million images from 1,000 object categories. While it was primed to be able to differentiate cats from dogs, the researchers needed it to know a malignant carcinoma from a benign seborrheic keratosis.

"There's no huge dataset of skin cancer that we can just train our algorithms on, so we had to make our own," said Brett Kuprel, co-lead author of the paper and a graduate student in the Thrun lab. "We gathered images from the internet and worked with the medical school to create a nice taxonomy out of data that was very messy - the labels alone were in several languages, including German, Arabic and Latin."

After going through the necessary translations, the researchers collaborated with dermatologists at Stanford Medicine, as well as Helen M. Blau, professor of microbiology and immunology at Stanford and co-author of the paper. Together, this interdisciplinary team worked to classify the hodgepodge of internet images. Many of these, unlike those taken by medical professionals, were varied in terms of angle, zoom and lighting. In the end, they amassed about 130,000 images of skin lesions representing over 2,000 different diseases.

During testing, the researchers used only high-quality, biopsy-confirmed images provided by the University of Edinburgh and the International Skin Imaging Collaboration Project that represented the most common and deadliest skin cancers -- malignant carcinomas and malignant melanomas. The 21 dermatologists were asked whether, based on each image, they would proceed with biopsy or treatment, or reassure the patient. The researchers evaluated success by how well the dermatologists were able to correctly diagnose both cancerous and non-cancerous lesions in over 370 images.

The algorithm's performance was measured through the creation of a sensitivity-specificity curve, where sensitivity represented its ability to correctly identify malignant lesions and specificity represented its ability to correctly identify benign lesions. It was assessed through three key diagnostic tasks: keratinocyte carcinoma classification, melanoma classification, and melanoma classification when viewed using dermoscopy. In all three tasks, the algorithm matched the performance of the dermatologists with the area under the sensitivity-specificity curve amounting to at least 91 percent of the total area of the graph.

An added advantage of the algorithm is that, unlike a person, the algorithm can be made more or less sensitive, allowing the researchers to tune its response depending on what they want it to assess. This ability to alter the sensitivity hints at the depth and complexity of this algorithm. The underlying architecture of seemingly irrelevant photos -- including cats and dogs -- helps it better evaluate the skin lesion images.

Health care by smartphone

Although this algorithm currently exists on a computer, the team would like to make it smartphone compatible in the near future, bringing reliable skin cancer diagnoses to our fingertips.

"My main eureka moment was when I realized just how ubiquitous smartphones will be," said Esteva. "Everyone will have a supercomputer in their pockets with a number of sensors in it, including a camera. What if we could use it to visually screen for skin cancer? Or other ailments?"

The team believes it will be relatively easy to transition the algorithm to mobile devices but there still needs to be further testing in a real-world clinical setting.

"Advances in computer-aided classification of benign versus malignant skin lesions could greatly assist dermatologists in improved diagnosis for challenging lesions and provide better management options for patients," said Susan Swetter, professor of dermatology and director of the Pigmented Lesion and Melanoma Program at the Stanford Cancer Institute, and co-author of the paper. "However, rigorous prospective validation of the algorithm is necessary before it can be implemented in clinical practice, by practitioners and patients alike."

Even in light of the challenges ahead, the researchers are hopeful that deep learning could someday contribute to visual diagnosis in many medical fields.
-end-
Additional Stanford co-authors of this work include Roberto Novoa, clinical assistant professor of dermatology and of pathology, and Justin Ko, clinical associate professor of dermatology. Thrun is also founder and president of Udacity. Blau is also the director of the Baxter Laboratory for Stem Cell Biology and a member of Stanford Bio-X, the Stanford Cardiovascular Institute, the Child Health Research Institute and the Stanford Cancer Institute.

Stanford University

Related Health Care Articles:

Mental health of health care workers in china in hospitals with patients with COVID-19
This survey study of almost 1,300 health care workers in China at 34 hospitals equipped with fever clinics or wards for patients with COVID-19 reports on their mental health outcomes, including symptoms of depression, anxiety, insomnia and distress.
Large federal program aimed at providing better health care underfunds primary care
Despite a mandate to help patients make better-informed health care decisions, a ten-year research program established under the Affordable Care Act has funded a relatively small number of studies that examine primary care, the setting where the majority of patients in the US receive treatment.
International medical graduates care for Medicare patients with greater health care needs
A study by a Massachusetts General Hospital research team indicates that internal medicine physicians who are graduates of medical schools outside the US care for Medicare patients with more complex medical needs than those cared for by graduates of American medical schools.
The Lancet Global Health: Improved access to care not sufficient to improve health, as epidemic of poor quality care revealed
Of the 8.6 million deaths from conditions treatable by health care, poor-quality care is responsible for an estimated 5 million deaths per year -- more than deaths due to insufficient access to care (3.6 million) .
Under Affordable Care Act, Americans have had more preventive care for heart health
By reducing out-of-pocket costs for preventive treatment, the Affordable Care Act appears to have encouraged more people to have health screenings related to their cardiovascular health.
High-deductible health care plans curb both cost and usage, including preventive care
A team of researchers based at IUPUI has conducted the first systematic review of studies examining the relationship between high-deductible health care plans and the use of health care services.
Health insurance changes, access to care by patients' mental health status
A research letter published by JAMA Psychiatry examined access to care before the Patient Protection and Affordable Care Act (ACA) and after the ACA for patients grouped by mental health status using a scale to assess mental illness in epidemiologic studies.
Medical expenditures rise in most categories except primary care physicians and home health care
This article was published in the July/August 2017 issue of Annals of Family Medicine research journal.
Care management program reduced health care costs in Partners Pioneer ACO
Pesearchers at Partners HealthCare published a study showing that Partners Pioneer ACO not only reduces spending growth, but does this by reducing avoidable hospitalizations for patients with elevated but modifiable risks.
Health care leaders predict patients will lose under President Trump's health care plans
According to a newly released NEJM Catalyst Insights Report, health care executives and industry insiders expect patients -- more than any other stakeholder -- to be the big losers of any comprehensive health care plan from the Trump administration.
More Health Care News and Health Care Current Events

Trending Science News

Current Coronavirus (COVID-19) News

Top Science Podcasts

We have hand picked the top science podcasts of 2020.
Now Playing: TED Radio Hour

Teaching For Better Humans 2.0
More than test scores or good grades–what do kids need for the future? This hour, TED speakers explore how to help children grow into better humans, both during and after this time of crisis. Guests include educators Richard Culatta and Liz Kleinrock, psychologist Thomas Curran, and writer Jacqueline Woodson.
Now Playing: Science for the People

#556 The Power of Friendship
It's 2020 and times are tough. Maybe some of us are learning about social distancing the hard way. Maybe we just are all a little anxious. No matter what, we could probably use a friend. But what is a friend, exactly? And why do we need them so much? This week host Bethany Brookshire speaks with Lydia Denworth, author of the new book "Friendship: The Evolution, Biology, and Extraordinary Power of Life's Fundamental Bond". This episode is hosted by Bethany Brookshire, science writer from Science News.
Now Playing: Radiolab

Space
One of the most consistent questions we get at the show is from parents who want to know which episodes are kid-friendly and which aren't. So today, we're releasing a separate feed, Radiolab for Kids. To kick it off, we're rerunning an all-time favorite episode: Space. In the 60's, space exploration was an American obsession. This hour, we chart the path from romance to increasing cynicism. We begin with Ann Druyan, widow of Carl Sagan, with a story about the Voyager expedition, true love, and a golden record that travels through space. And astrophysicist Neil de Grasse Tyson explains the Coepernican Principle, and just how insignificant we are. Support Radiolab today at Radiolab.org/donate.